CN116047909B - Unmanned plane-ship cooperative robust self-adaptive control method for maritime parallel search - Google Patents
Unmanned plane-ship cooperative robust self-adaptive control method for maritime parallel search Download PDFInfo
- Publication number
- CN116047909B CN116047909B CN202310067964.7A CN202310067964A CN116047909B CN 116047909 B CN116047909 B CN 116047909B CN 202310067964 A CN202310067964 A CN 202310067964A CN 116047909 B CN116047909 B CN 116047909B
- Authority
- CN
- China
- Prior art keywords
- representing
- uav
- usv
- ith
- sensor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 239000011159 matrix material Substances 0.000 claims abstract description 49
- 238000013178 mathematical model Methods 0.000 claims abstract description 18
- 230000003044 adaptive effect Effects 0.000 claims abstract description 11
- 238000013461 design Methods 0.000 claims description 25
- 230000015572 biosynthetic process Effects 0.000 claims description 8
- 238000013016 damping Methods 0.000 claims description 8
- 230000001537 neural effect Effects 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 4
- 230000001419 dependent effect Effects 0.000 claims description 2
- 238000006467 substitution reaction Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 230000008054 signal transmission Effects 0.000 abstract description 3
- 238000004088 simulation Methods 0.000 description 7
- 238000001514 detection method Methods 0.000 description 6
- 230000007246 mechanism Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 230000001960 triggered effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004007 neuromodulation Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a marine parallel search oriented unmanned aerial vehicle-ship cooperative robust self-adaptive control method, which comprises the following steps: establishing a nonlinear mathematical model of the USV and a nonlinear mathematical model of the UAV; acquiring a control input matrix of the USV-UAV cooperative system; establishing a sensor fault model of the USV-UAV cooperative system; acquiring a reference track of the USV to acquire a real-time reference route track of the UAV; acquiring a reference position signal of the USV and a reference attitude signal of the UAV; an adaptive controller of the USV-UAV co-system is obtained to control the USV-UAV co-system. The invention fully considers the complexity of the USV-UAV cooperative system and the unstable influence of sensor signal loss caused by the interference of the external ocean environment on the cooperative control system, solves the problem that the sensor signal transmission is easy to lose in the ocean environment, and improves the guidance reliability of the USV-UAV cooperative system.
Description
Technical Field
The invention relates to the technical fields of ship-unmanned aerial vehicle cooperative control engineering and maritime search and rescue application, in particular to an unmanned aerial vehicle-ship cooperative robust self-adaptive control method for maritime parallel search.
Background
The cooperative path tracking control of the USV-UAV is a research hotspot in the world at present, aiming at the guidance framework, the guidance mechanism is mainly realized from the theoretical angle, the complexity of the cooperative system of the USV-UAV and the unstable influence of sensor signal loss caused by the interference of the external marine environment on the cooperative control system cannot be fully considered, and compared with the single system of the USV or UAV, the cooperative system of the USV-UAV has more sensors, needs more frequent internal and external sensor signal transmission and is easier to generate signal loss in the real marine environment.
Disclosure of Invention
The invention provides a marine parallel search oriented unmanned aerial vehicle-ship cooperative robust self-adaptive control method, which aims to overcome the technical problems.
A marine parallel search oriented unmanned plane-ship collaborative robust self-adaptive control method comprises the following steps:
s1: establishing a nonlinear mathematical model of the USV and a nonlinear mathematical model of the UAV;
s2: acquiring a control input matrix of a USV-UAV cooperative system according to the nonlinear mathematical model of the USV and the nonlinear mathematical model of the UAV;
s3: establishing a sensor fault model of the USV-UAV cooperative system;
s4: acquiring a reference track of the USV to acquire a real-time reference route track of the UAV;
s5: acquiring a reference position signal of the USV and a reference attitude signal of the UAV according to the reference track of the USV and the real-time reference track of the UAV;
s6: and acquiring an adaptive controller of the USV-UAV cooperative system according to the control input matrix of the USV-UAV cooperative system, the sensor fault model of the USV-UAV cooperative system, the reference position signal of the USV and the reference posture signal of the UAV so as to control the USV-UAV cooperative system.
The beneficial effects are that: according to the unmanned aerial vehicle-ship cooperative robust self-adaptive control method for parallel maritime search, through establishing the sensor fault model of the USV-UAV cooperative system, the complexity of the USV-UAV cooperative system and the unstable influence of sensor signal loss caused by external marine environment interference on the cooperative control system are fully considered, and the control input matrix of the USV-UAV cooperative system, the sensor fault model of the USV-UAV cooperative system, the reference position signal of the USV and the reference attitude signal of the UAV are combined to obtain the self-adaptive controller of the USV-UAV cooperative system, so that the problem that the sensor signal transmission is easy to lose in the marine environment is solved, and the reliability of guidance of the USV-UAV cooperative system is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a method for collaborative robust self-adaptive control of an unmanned aerial vehicle-ship in accordance with the present invention;
FIG. 2 is a USV-UAV collaborative marine parallel search guidance structure framework in accordance with embodiments of the invention;
FIG. 3 is a schematic diagram of a USV-UAV co-system in accordance with embodiments of the invention;
FIG. 4 is a graph of changes in heading angle for a USV in an embodiment of the invention;
FIG. 5 is a schematic diagram of a fault tolerant adaptive compensation law of a sensor in an embodiment of the present invention;
FIG. 6 is a schematic diagram of the control input curve of the USV of the invention;
FIG. 7 is a comparison of control algorithm time trigger intervals in an embodiment of the present invention;
FIG. 8 is a graph of trigger threshold parameter variation in an embodiment of the invention;
FIG. 9 is a diagram of a USV-UAV collaborative marine parallel search path trace in accordance with an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment provides a cooperative robust self-adaptive control method of an unmanned aerial vehicle-ship for maritime parallel search, as shown in fig. 1-3, comprising the following steps:
s1: establishing a nonlinear mathematical model of a 3-degree-of-freedom (unmanned ship) USV and a nonlinear mathematical model of a 6-degree-of-freedom (unmanned aerial vehicle) UAV;
the nonlinear mathematical model of the USV is built as follows:
the nonlinear mathematical model of the UAV is built as follows:
wherein ,
wherein ,ηa ＝[x ai ,y ai ,z ai ,ψ ai ,φ ai ,θ ai ] T, wherein ,xai Representing the spatial abscissa of the ith UAV; y is ai Representing the spatial ordinate of the ith UAV; z ai Representing the spatial vertical coordinates of the ith UAV; psi phi type ai Representing a heading angle of an ith UAV; phi (phi) ai Representing the roll angle of the ith UAV; θ ai Representing pitch angle of the ith UAV; η (eta) s ＝[x s ,y s ,ψ s ] T, wherein xs The position abscissa representing the USV; y is s A position ordinate representing USV; psi phi type s A heading angle representing USV; v (v) a ＝[u xi ,u yi ,u zi ,r ψi ,r φi ,r θi ] T, wherein uxi Representing the speed of the ith UAV along the ox axis; u (u) yi Representing the speed of the ith UAV along the oy axis; u (u) zi Representing the speed of the ith UAV along the oz axis; r is (r) ψi Representing the rotational angular velocity of the ith UAV along the ox axis; r is (r) φi Representing rotational angular velocity of the ith UAV along the oy axis; r is (r) θi Representing rotational angular velocity of the ith UAV along the oz axis; v (v) s ＝[u s ,v s ,r s ] T, wherein ,us Representing the forward speed of the USV; v s Represents the speed of the USV; r is (r) s Representing the yaw rate of the USV; j (eta) s ) A transformation matrix representing the USV; f (v) a )＝[f xi ,f yi ,f zi ,f ψi ,f φi ,f θi ] T A nonlinear term representing the ith UAV in 6 degrees of freedom; wherein f xi A nonlinear term representing the UAV along the x-axis; f (f) yi A nonlinear term representing the UAV along the y-axis; f (f) zi A nonlinear term representing the UAV along the z-axis; f (f) ψi A nonlinear term representing the UAV's freedom along the bow; f (f) φi A nonlinear term representing the UAV along the roll degrees of freedom; f (f) θi A nonlinear term representing the UAV along the pitch degrees of freedom;representing the gain matrix of the ith UAV, where R xi A gain matrix representing the UAV along the x-axis; r is R yi A gain matrix representing the UAV along the y-axis; r is R zi A gain matrix representing the UAV along the z-axis; r is R ψi A gain matrix representing a degree of freedom of the UAV along the bow; r is R φi A gain matrix representing the UAV along the roll degrees of freedom; r is R θi A gain matrix representing the UAV along pitch degrees of freedom; wherein R is xi ＝cos(φ ai )sin(θ ai )cos(ψ ai )+sin(θ ai )sin(ψ ai )，R yi ＝cos(φ ai )sin(θ ai )cos(ψ ai )-sin(θ ai )sin(ψ ai )，R zi ＝cos(φ ai )cos(ψ ai )，I zz Expressed in terms of rotational inertia along the ox axis in UAV, I yy Representing rotational inertia along the oy axis at the UAV, I xx Representing rotational inertia along the oz axis at the UAV, d representing the diagonal diameter of the drone; τ (v) a )＝[τ fi ,τ ψi ,τ φi ,τ θi ] T A control input matrix representing an ith UAV; wherein τ fi A control input representing the UAV in a vertical direction; τ ψi A control input representing the UAV in a yaw direction; τ φi A control input representing a UAV in a roll direction; τ θi A control input representing the UAV in a pitch direction; d, d w (ν a ) Representing disturbance force and moment caused by external environment to which the ith UAV is subjected in 6 degrees of freedom; wherein d w (ν a )＝[d xi ,d yi ,d zi ,d ψi ,d φi ,d θi ] T ，d xi Representing external interference force applied to the UAV in the x-axis direction; d, d yi Representing external interference force applied to the UAV in the y-axis direction; d, d zi Representing external interference forces experienced by the UAV in the z-axis direction; d, d ψi Representing external disturbance moment applied to the UAV in the bow-swing direction; d, d φi Representing the external disturbance moment to which the UAV is subjected in the roll direction; d, d θi Representing the external disturbance moment to which the UAV is subjected in the pitching direction; d (g) represents the gravitational acceleration matrix of the UAV in 6 degrees of freedom, where d= [0, -g,0] T Representing a gravitational acceleration matrix, g representing gravitational acceleration; m is M -1 (-) represents the inverse of the additional mass inertia matrix of the USV; c (v) s ) A Kernel matrix representing USV; f (v) s ) A nonlinear term representing USV, where f (v) s )＝[f u ,f v ,f r ] T ，f u A nonlinear term representing the USV in the forward degree of freedom; f (f) v A nonlinear term representing USV in lateral float degrees of freedom; f (f) r Representing a non-linear term of the USV in a yaw degree of freedom; τ (v) s ) Representing a control input matrix of USV, where τ (v) s )＝[τ u ,0,τ r ] T ，τ u Representing the propulsion provided by the USV propeller; τ r Representing the turning moment provided by the USV rudder; d, d w (ν s ) Representing disturbance forces and moments of the external marine environment to which the USV is subjected, where d w (ν s )＝[d wu ,d wv ,d wr ] T ，d wu Showing the disturbance force and moment of the external marine environment to which the USV is subjected in the forward degree of freedom; d, d wv Showing the disturbance force and moment of the external marine environment suffered by the USV on the lateral drift degree of freedom; d, d wr The disturbance force and moment of the external ocean environment of the USV on the bow swing degree of freedom are represented; m represents an additional mass inertia matrix of the USV; m is m u Represents the hydrodynamic additional mass of the USV in the forward direction, m v Represents the hydrodynamic additional mass of USV in the lateral drift direction, m r Represents the hydrodynamic additional mass of USV in the yaw direction, m s Representing the quality of the USV; />Represents the hydrodynamic derivative of USV in the forward direction,/->Representing the hydrodynamic derivative of the USV in the lateral drift direction;
s2: acquiring a control input matrix of a USV-UAV cooperative system according to the nonlinear mathematical model of the USV and the nonlinear mathematical model of the UAV;
the control input matrix of the USV-UAV cooperative system is obtained as follows:
τ＝G(ν s ,ν a )ξ o (4)
wherein ,
where τ represents a control input matrix of the USV-UAV co-system, where τ= [ τ (v) s ),τ(ν a )] T ；G(ν s ,ν a ) A control input gain matrix representing the USV-UAV co-system; zeta type toy o A control command matrix representing a USV-UAV co-system, wherein ζ o ＝[|ξ δ ,|ξ n |ξ n ,|ξ 1i |ξ 1i ,|ξ 2i |ξ 2i ,|ξ 3i |ξ 3i ,|ξ 4i |ξ 4i ] T ，ξ δ Represents the rudder angle, ζ of USV n Represents the host rotational speed, ζ of USV 1i ,ξ 2i ,ξ 3i ,ξ 4i Representing rotational speed of 4 rotor rotors of the ith UAV, F r Steering engine gain function, T, representing USV u Propeller gain function, k, representing USV p Representing parameters dependent on the geometry of the blade and the air density c d Representing the resistance coefficient; |·| represents absolute value operations;
s3: establishing a sensor fault model of the USV-UAV cooperative system;
in control engineering, the position signals of the actual USV and UAV are measured by associated sensors, such as global positioning system, electronic compass, etc. In practice, sensor signal loss or partial loss may result from the presence of sensor/channel failure, which may reduce the safety of the cooperative control system. The present invention is therefore directed to introducing a sensor fault model for status signals (i.e., position signals of USV and UAV) as follows:
in the formula ,representing the sensor output of the unmanned ship USV when j=s, and the unmanned UAV when j=a; ρ η Indicating failure coefficient, ζ of sensor η Representing a fault paranoid coefficient of the sensor;
s4: acquiring a reference track of the USV to acquire a real-time reference route track of the UAV; assuming that the reference trajectory of the USV is generated by VS i.e. Virtual ship model (Virtual ship) real-time planning,
the reference trajectory of the USV is acquired as follows:
in the formula ,xsv Represents the x-direction position coordinates of VS, y sv Represents the y-direction position coordinates of VS, ψ sv Indicating the heading angle of VS, u v Represents the forward speed of VS; v v Represents the yaw rate of VS; r is (r) v A yaw rate representing VS;representing a derivative operation;
specifically, in maritime search tasks, a plurality of UAVs are generally configured on two sides of the USV to fully exploit the detection advantages of the UAVs. To achieve this objective, the present embodiment constructs the real-time reference course trajectory on the spatial plane of VA according to the information of VS as follows:
in the formula ,xavi Representing the spatial plane x-direction position coordinates of the ith VA; y is avi Representing the spatial plane y-direction position coordinates of the ith VA; psi phi type avi Representing the spatial plane heading angle of the ith VA; zeta type di A formation distance representing the ith VA; lambda (lambda) di A formation direction angle representing the ith VA;
s5: according to the reference track of the USV and the real-time reference track of the UAV, the reference position signal of the USV and the reference attitude signal of the UAV are obtained as follows:
in the formula ,ψsd Representing the reference heading angle, x of the USV se Represents the horizontal coordinate difference between USV and VS, y se Representing the difference between USV and VS; the reference attitude signal of the UAV is obtained as follows:
in the formula ：ψadi Representing a reference heading of an ith UAV; phi (phi) adi A reference roll angle representing an ith UAV; θ adi Representing a reference pitch angle of the ith UAV; x is x aei Representing the difference of the horizontal coordinates of the ith USV and the ith VA on the space plane; y is aei Representing the difference of the vertical coordinates of the ith USV and the ith VA on the space plane; c x Representing a position controller of the ith UAV along the x-axis; c y Representing a position controller of the ith UAV along the y-axis; c z Representing a position controller of the ith UAV along the z-axis;
s6: and acquiring an adaptive controller of the USV-UAV cooperative system by using a backstepping technology according to the sensor fault model of the USV-UAV cooperative system, the reference position signal of the USV and the reference posture signal of the UAV so as to control the USV-UAV cooperative system.
S61: acquiring a position error and an attitude error of the USV-UAV cooperative system according to the sensor fault model of the USV-UAV cooperative system and a reference position signal of the USV and a reference attitude signal of the UAV;
the position error and attitude error of the USV-UAV co-system were obtained as follows:
in the formula ,ψse Representing the heading difference of the USV,representing USVThe inverse of the failure coefficient of the x-coordinate in the position sensor,inverse of the failure coefficient representing the y-coordinate in the position sensor of the USV, +.>Representing the inverse, ζ of the failure coefficient of the yaw sensor of the USV sx Paranoid fault coefficient, ζ, representing x-coordinate in position sensor of USV sy Paranoid fault coefficient, ζ, representing y-coordinate in position sensor of USV sψ A yaw failure coefficient representing a yaw sensor of a USV, +.>X-coordinate output signal of position sensor representing USV,/v>Y-coordinate output signal of position sensor representing USV,/v>A heading sensor output signal representative of the USV; z aei Represents the vertical coordinate difference, ψ, of the ith UAV aei Representing a heading difference of an ith UAV; phi (phi) aei Representing the roll difference of the ith UAV; θ aei Representing pitch difference of the ith UAV; />Inverse of the failure coefficient representing the x-coordinate in the position sensor of the ith UAV, +.>Inverse of the failure coefficient representing the y-coordinate in the position sensor of the ith UAV, +.>Inverse of the failure coefficient representing the z-coordinate in the position sensor of the ith UAV, +.>Representing the inverse of the failure coefficient of the yaw sensor of the ith UAV,/and>representing the inverse of the failure coefficient of the roll sensor of the ith UAV, +.>Representing the inverse of the failure coefficient of the pitch sensor of the ith UAV. Zeta type axi Paranoid fault coefficient, ζ, representing the x-coordinate in the position sensor of the ith UAV ayi Paranoid fault coefficient, ζ, representing y-coordinate in position sensor of ith UAV azi Paranoid fault coefficient, ζ, representing z-coordinate in position sensor of ith UAV aψi Paranoid fault coefficient, ζ, representing the ith UAV's yaw sensor aφi Paranoid fault coefficient, ζ, representing the roll sensor of the ith UAV aθi Representing the paranoid fault coefficient of the pitch sensor of the ith UAV.
Output signal representing the x-coordinate of the position sensor of the UAV,/for>Output signal representing the x-coordinate of the position sensor of the UAV,/for>Output signal representing the y-coordinate of the position sensor of the UAV, +.>Output signal indicative of a yaw sensor of a UAV,/->Output signal representing roll sensor of UAV,/->An output signal representative of a pitch sensor of the UAV; specifically, for VA, the vertical reference coordinate z avi Is typically set to a constant value by human.
S62: obtaining a virtual control law to eliminate a position error and an attitude error of a USV-UAV cooperative system; specifically, in order to calm the position error and the attitude error of the USV-UAV cooperative system, a corresponding virtual control law and a sensing fault self-adaptive compensation law are designed as follows:
the virtual control law is obtained as follows:
the sensor fault self-adaptive compensation law is acquired to acquire an optimized virtual control law as follows: the self-adaptive compensation law of the sensing faults is used for updating and calculating parameters in the virtual control law through a formula (13), and compensating in the virtual control law:
in the formula ,αus Representing the virtual control law of the USV in the forward direction;k sψ ,k ap ,,/>all are positive design parameters;representing the straight line distance of the USV to the reference position; delta △ Position boundary parameters representing USV; />Representing the middleReplacement variable, wherein->△ sx Representing a positive design parameter of the USV for the hyperbolic tangent function in the forward direction; alpha rs Representing a virtual control law of the USV in a bow-swing direction; alpha pai Representing the position virtual control law of UAVs, +.>Representing the attitude virtual control law of a UAV, where p=x, y, z, +.>△ sψ Representing a positive design parameter of the USV for the hyperbolic tangent function in the yaw direction; />Representing positive design parameters of the UAV for the hyperbolic tangent function in the pose direction; />Representing a derivative operation; />An estimate of (-); />Representation->Is a derivative value of (a); p is p aei Representing a position error of the UAV; />Representing an attitude error of the UAV; p is p avi Representing a reference position of the UAV; />Representing a reference pose of the UAV;
γ x1 ,γ x2 ,γ ψ1 ,γ ψ2 ,γ p1 ,γ p2 ,σ x1 ,σ x2 ,σ ψ1 ,σ ψ2 ,σ p1 ,σ p2 ,/>all represent positive adaptive design parameters; />Inverse of the failure coefficient representing the x-coordinate in the position sensor of the USV, +.>Inverse of the failure coefficient of the yaw sensor, representing the USV, < >>Inverse of the failure coefficient representing the attitude sensor of the UAV,/->Inverse, pi, representing the failure coefficient of the position sensor of the UAV sx Derivative of the paranoid coefficient, pi, representing a USV position sensor fault sψ Derivative of the paranoid coefficient representing a USV yaw sensor failure, +.>Derivatives of the paranoid coefficients representing UAV attitude sensor faults; pi-shaped structure api Representing the inverse of the failure coefficient of the position sensor of the UAV; />Representation->Is set to an initial value of (1); />Representation->Is set to an initial value of (1);representation->Is set to an initial value of (1); />Representation->Is set to an initial value of (1); pi-shaped structure sx (0) Representing pi sx Is set to an initial value of (1); pi-shaped structure sψ (0) Representing pi sψ Is set to an initial value of (1); pi-shaped structure api (0) Representing pi api Is set to an initial value of (1); />Representation->Is set to an initial value of (1); specifically, the virtual control law can be obtained in the embodiment to realize high fault tolerance performance of the USV-UAV collaborative system in maritime plane search task.
Since the virtual control law causes a great computational load problem in the following derivation, a dynamic surface technology is introduced to perform reduced order processing on the derivative of the virtual controller, which is a common processing mode in the prior art.
S63: acquiring a speed error of the USV-UAV cooperative system according to the optimized virtual control law so as to acquire a derivative of the speed error of the USV-UAV cooperative system;
let u se ＝u s -α us ，r se ＝r s -α rs ，u pei ＝u pi -α upi ，To obtain the derivative of the dynamic surface signal of the virtual control law; the derivative of the speed error of the USV-UAV co-system is obtained as follows:
in the formula ,use Representing the speed error of the USV; r is (r) se Representing a yaw rate error of the USV; u (u) pei Representing a position velocity error of the UAV; u (u) pi Representing a position velocity of the UAV; alpha upi Representing a position virtual control law of the UAV;representing an attitude velocity error of the UAV; />Representing a gesture velocity of the UAV; />Representing a virtual control law of a pose of the UAV; m is m u Representing the hydrodynamic additional mass of the USV in the forward direction; f (f) u A nonlinear term representing the USV in the forward direction; t (T) u Propeller gain coefficient representing USV; d, d wu Representing the disturbance force experienced by the USV in the forward direction; />Representing alpha us Is a derivative of the dynamic surface signal; />Representing alpha us The derivative of the dynamic face error of (a); m is m r Representing the hydrodynamic additional mass of the USV in the yaw direction; f (f) r Representing the non-linear term of the USV in the yaw direction; f (F) r Gain factor of steering engine representing USV; d, d wr Indicating that USV is at the bowDisturbance moment applied in the shaking direction; />Representing alpha rs Is a derivative of the dynamic surface signal; />Representing alpha rs The derivative of the dynamic face error of (a); f (f) pi A position uncertainty term representing the UAV;
R pi representing a position gain matrix of the UAV; d, d wxi Representing external interference force applied to the UAV in the x-axis direction;representing alpha upi Is a derivative of the dynamic surface signal; />Representing alpha upi The derivative of the dynamic face error of (a); />Representing a pose gain matrix of the UAV;representing an attitude disturbance moment of the UAV; />Representation->Is a derivative of the dynamic surface signal; m is m a Representing the quality of the UAV; (. Cndot. -1 Representing an inverse function operation;
for model uncertainty term in equation (14), i.e., f u ,f r ,f pi ,The present embodiment is processed using the robust neural damping technique of the prior art.
Furthermore, because of the large communication load present in the USV-UAV collaborative system, the present invention introduces an event trigger mechanism for control force/torque and designs an improved dynamic trigger threshold parameter. A specific event triggering mechanism can be described as equation (15).
S64: establishing event triggering rules of the USV-UAV cooperative system:
in the formula ,cw1 (t w ),c w2 (t w ) Representing a trigger threshold parameter that is set to be equal to or greater than a threshold value,a control input representing the trigger time of the cooperating body;representing a current trigger point in time of the collaborative entity within different channels; τ w A control input representing a collaborative volume; t is t w Representing control times of the cooperators in different channels; t represents a trigger time; />Representing a next trigger point in time of the collaborative entity within a different channel; fi represents the lift direction of the UAV; />A gesture variable representing the UAV; e, e w A difference between the control input indicating the trigger time and the control input indicating the non-trigger time;
the embodiment introduces the state error of the USV-UAV cooperative system into the trigger threshold parameter, and realizes the self-adaptive adjustment of the trigger threshold parameter based on engineering requirements, namely, the formula (17).
in the formula ,c0 Representing a positive minimum trigger parameter, η e Representing a state error of the USV-UAV collaboration system.
Therefore, it is possible to obtain,
in the formula ,λw1 and λw2 All represent trigger range defining parameters; c w1 (t w) and cw2 (t w ) All represent trigger threshold parameters; specifically, the event triggering rule in this embodiment enables the USV-UAV collaboration system to perform a specific low-traffic load performance in performing a maritime plane search task.
S65: substituting formula (18) into formula (14) according to event triggering rules of the USV-UAV collaboration system, and acquiring an intermediate controller as follows:
in the formula ,ksu ,k sr Representing controller design parameters, k, of USV ap ,Representing controller design parameters of the UAV. k (k) un ,k rn Robust neural damping design parameters, k, representing USV pn ,/>Representing the robust neuromodulation design parameters of the UAV. Phi u ,Φ r Robust neural damping term, Φ, representing USV pi ,/>Representing the robust neural damping term for the ith UAV.
It is derived that the method comprises the steps of,
s66: the adaptive controller (control command matrix of the USV-UAV cooperative system) that acquires the USV-UAV cooperative system is as follows: specifically, by combining the formula (4), it is possible to obtain,
finally, the actual control command xi of the USV-UAV cooperative system can be obtained δ ,ξ n ,ξ 1i ,ξ 2i ,ξ 3i ,ξ 4i 。
In order to verify the superiority and effectiveness of the control scheme provided by the invention, 2 numerical experiments are carried out on a Matlab simulation platform. The simulation case A is a comparison experiment of the method of the embodiment and the existing event triggering technology, and the simulation case B is a USV-UAV collaborative system path tracking experiment simulating parallel search of maritime affairs.
Simulation case a: the algorithm of the embodiment is compared with the existing method (static event-triggered control (SETC) and dynamic event-triggered control (DETC)), and in order to display the comparison effect more simply and intuitively, the heading maintaining control of the USV is selected as a comparison task, and the expected heading of the USV is 60deg. Wherein, the setting of event triggering rules of 3 algorithms is shown in table 1, and the NDETC represents the novel dynamic event triggering control (novel dynamic event-triggered, NDETC) proposed by the present invention.
Table 13 event trigger rule set-up cases for algorithms
The response curves of the USV under the three algorithms are shown in fig. 4-8. FIG. 4 is a heading angle comparison for three algorithms. As can be seen from fig. 4, the heading angles of the three algorithms can reach the control target with a small tracking error. In addition, when the sensor fault occurrence time is 20s-30s, the actual output curve adopting the self-adaptive fault-tolerant compensation law (figure 5) has better tracking performance. It should also be noted that the adaptive fault tolerance compensation law is only executed if the sensor fails. Although the 3 algorithms all achieve similar control performance, the control inputs are different at different channel transmission loads. From fig. 6, we can find that the inventive algorithm saves the number of control command generation compared to DETC and SETC based control commands. To intuitively display the comparison effect, the trigger intervals for the 3 algorithms are shown in fig. 7. Wherein, the SETC trigger times is 164 times, the DETC trigger times is 100 times, and the trigger times of the algorithm of the invention is 76 times. In addition, in the initial state, the trigger interval of the invention is obviously larger than that of other methods. As shown in fig. 8, the threshold parameter curve pair of the threshold parameter proposed by the present invention and the threshold parameter curve pair of the existing DETC can be seen from fig. 8, the trigger threshold parameter of the algorithm of the present invention can be adaptively adjusted according to the state error, instead of continuously converging in the vicinity of zero.
Simulation case B: under the condition of simulating external interference, the method of the embodiment realizes the maritime parallel search task under the condition of no detection blind area. The search sea area consists of 10 waypoints, namely W1 (0 m;0 m), W2 (800 m;0 m), W3 (800 m;1000 m), W4 (160 m;1000 m), W5 (160 m;0 m), W6 (2400 m;0 m), W7 (2400 m;1000 m), W8 (3200 m;1000 m), W9 (3200 m;0 m), W10 (4000 m;0 m). The search radii of the USV and UAV were set to 100m and 150m, respectively.
The main results of the parallel search of the USV-UAV collaboration system are shown in FIG. 9. Fig. 9 (a) and (b) are a three-dimensional space view and a two-dimensional top view, respectively, of a maritime cooperation parallel search path. As is evident from fig. 9, 2 UAVs and 1 USV can perform a collaborative parallel search task. This approach can increase search area with lower economies than multiple USV formation searches. The co-detection area can be extended by a factor of 4 compared to a single USV. In addition, the maneuverability difference of the USV-UAV is considered in the design of the guidance algorithm, so that the detection blind area near the waypoint is avoided.
Combining the prior art, the controller design and 2 simulation cases, the invention has the following 2 beneficial effects in the field of collaborative maritime search:
1) The cooperative robust self-adaptive control method for the USV-UAV based on maritime parallel search mainly comprises two parts of guidance and control, and a 3D maritime parallel search guidance strategy considering the operability of the USV and the UAV is constructed in a preparation part, wherein in the extracted guidance strategy, the VS-VA can cooperatively plan the reference paths of the USV and the UAV in real time, so that the formation structure of the USV-UAV is ensured. In a control part, a novel dynamic event trigger mechanism and a sensor fault-tolerant self-adaptive compensation law are provided, and a USV-UAV cooperative robust self-adaptive control algorithm is designed. The algorithm can realize effective control of the USV-UAV cooperative system, and has the advantages of good fault tolerance and low communication load.
2) Through 2 simulation cases, the method has certain advantages, especially in aspects of reducing the internal communication load of a collaborative system and improving the fault tolerance of sensor signals. Meanwhile, the method also realizes the maritime parallel search task without the detection blind area, and has important promotion effect on accelerating the application of the USV-UAV cooperative system in the maritime engineering field.
The method of the embodiment has the following characteristics:
the control problem that the UAV is located right above the USV and performs the cooperative path tracking task is mainly solved.
Aiming at actual maritime engineering tasks, an unmanned aerial vehicle-ship collaborative robust self-adaptive control method for maritime parallel search is established, wherein the unmanned aerial vehicle-ship collaborative robust self-adaptive control method is a collaborative 3D maritime parallel search guidance strategy based on a virtual ship and a virtual aircraft (VS-VA), in the guidance strategy, a real-time reference path of a USV can be planned by a VS according to waypoint information, and meanwhile, a reference route of a UAV can be established by the VA according to information of the VS according to a collaborative formation structure. In addition, the maneuverability difference of the USV-UAV is considered in the guidance design, so that the cooperative formation guidance of the USV-UAV can be realized, and the detection blind area existing near the waypoint can be avoided.
Aiming at the communication load limitation of the USV-UAV cooperative control system, an improved dynamic event triggering mechanism is provided, and asynchronous triggering of a USV channel and a UAV channel can be realized. Compared with the artificial design threshold parameter and the dynamic threshold parameter in the prior art, the dynamic event triggering strategy of the improved version provided by the invention can adaptively adjust the triggering threshold parameter according to the state error of the control system, namely, the factor of the state error of the control system is considered in the triggering threshold parameter. This avoids the limitation of manually set parameters, reduces the number of design parameters in the control system, and also avoids the limitation of converging to zero.
Aiming at the problems that a system is complex and the unstable control system caused by sensor signal loss exists in a USV-UAV cooperative system, the invention constructs a sensor signal fault model, designs a self-adaptive fault-tolerant compensation law aiming at a sensor signal failure coefficient and a paranoid coefficient, and realizes effective compensation of sensor signal faults.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (1)
1. The unmanned aerial vehicle-ship collaborative robust self-adaptive control method for parallel search of maritime is characterized by comprising the following steps of:
s1: establishing a nonlinear mathematical model of the USV and a nonlinear mathematical model of the UAV;
s2: acquiring a control input matrix of a USV-UAV cooperative system according to the nonlinear mathematical model of the USV and the nonlinear mathematical model of the UAV;
s3: establishing a sensor fault model of the USV-UAV cooperative system;
s4: acquiring a reference track of the USV to acquire a real-time reference route track of the UAV;
s5: acquiring a reference position signal of the USV and a reference attitude signal of the UAV according to the reference track of the USV and the real-time reference track of the UAV;
s6: acquiring an adaptive controller of the USV-UAV cooperative system according to a control input matrix of the USV-UAV cooperative system, a sensor fault model of the USV-UAV cooperative system, a reference position signal of the USV and a reference posture signal of the UAV so as to control the USV-UAV cooperative system; in the step S1, a nonlinear mathematical model of the USV is established as follows:
the nonlinear mathematical model of the UAV is built as follows:
wherein ,
wherein ,ηa ＝[x ai ,y ai ,z ai ,ψ ai ,φ ai ,θ ai ] T, wherein ,xai Representing the spatial abscissa of the ith UAV; y is ai Representing the spatial ordinate of the ith UAV; z ai Representing the spatial vertical coordinates of the ith UAV; psi phi type ai Representing a heading angle of an ith UAV; phi (phi) ai Representing the roll angle of the ith UAV; θ ai Representing pitch angle of the ith UAV; η (eta) s ＝[x s ,y s ,ψ s ] T, wherein xs The position abscissa representing the USV; y is s A position ordinate representing USV; psi phi type s A heading angle representing USV; v (v) a ＝[u xi ,u yi ,u zi ,r ψi ,r φi ,r θi ] T, wherein uxi Representing the ith UAV alongVelocity of ox axis; u (u) yi Representing the speed of the ith UAV along the oy axis; u (u) zi Representing the speed of the ith UAV along the oz axis; r is (r) ψi Representing the rotational angular velocity of the ith UAV along the ox axis; r is (r) φi Representing rotational angular velocity of the ith UAV along the oy axis; r is (r) θi Representing rotational angular velocity of the ith UAV along the oz axis; v (v) s ＝[u s ,v s ,r s ] T, wherein ,us Representing the forward speed of the USV; v s Represents the speed of the USV; r is (r) s Representing the yaw rate of the USV; j (eta) s ) A transformation matrix representing the USV; f (v) a )＝[f xi ,f yi ,f zi ,f ψi ,f φi ,f θi ] T A nonlinear term representing the ith UAV in 6 degrees of freedom; wherein f xi A nonlinear term representing the UAV along the x-axis; f (f) yi A nonlinear term representing the UAV along the y-axis; f (f) zi A nonlinear term representing the UAV along the z-axis; f (f) ψi A nonlinear term representing the UAV's freedom along the bow; f (f) φi A nonlinear term representing the UAV along the roll degrees of freedom; f (f) θi A nonlinear term representing the UAV along the pitch degrees of freedom;representing the gain matrix of the ith UAV, where R xi A gain matrix representing the UAV along the x-axis; r is R yi A gain matrix representing the UAV along the y-axis; r is R zi A gain matrix representing the UAV along the z-axis; r is R ψi A gain matrix representing a degree of freedom of the UAV along the bow; r is R φi A gain matrix representing the UAV along the roll degrees of freedom; r is R θi A gain matrix representing the UAV along pitch degrees of freedom; τ (v) a )＝[τ fi ,τ ψi ,τ φi ,τ θi ] T A control input matrix representing an ith UAV; wherein τ fi A control input representing the UAV in a vertical direction; τ ψi A control input representing the UAV in a yaw direction; τ φi A control input representing a UAV in a roll direction; τ θi A control input representing the UAV in a pitch direction; d, d w (ν a ) Indicating that the ith UAV is free at 6Disturbance force and moment caused by external environment to which the degree is subjected; wherein d w (ν a )＝[d xi ,d yi ,d zi ,d ψi ,d φi ,d θi ] T ，d xi Representing external interference force applied to the UAV in the x-axis direction; d, d yi Representing external interference force applied to the UAV in the y-axis direction; d, d zi Representing external interference forces experienced by the UAV in the z-axis direction; d, d ψi Representing external disturbance moment applied to the UAV in the bow-swing direction; d, d φi Representing the external disturbance moment to which the UAV is subjected in the roll direction; d, d θi Representing the external disturbance moment to which the UAV is subjected in the pitching direction; d (g) represents the gravitational acceleration matrix of the UAV in 6 degrees of freedom; m is M -1 (-) represents the inverse of the additional mass inertia matrix of the USV; c (v) s ) A Kernel matrix representing USV; f (v) s ) Representing the nonlinear term of USV, τ (v) s ) Representing a control input matrix of USV, where τ (v) s )＝[τ u ,0,τ r ] T ，τ u Representing the propulsion provided by the USV propeller; τ r Representing the turning moment provided by the USV rudder; d, d w (ν s ) Representing the disturbance force and moment of the external ocean environment to which the USV is subjected, and M represents the additional mass inertia matrix of the USV; m is m u Represents the hydrodynamic additional mass of the USV in the forward direction, m v Represents the hydrodynamic additional mass of USV in the lateral drift direction, m r Represents the hydrodynamic additional mass of USV in the yaw direction, m s Representing the quality of the USV; />Represents the hydrodynamic derivative of USV in the forward direction,/->Representing the hydrodynamic derivative of the USV in the lateral drift direction;
in the step S2 of the above-mentioned method,
the control input matrix of the USV-UAV cooperative system is obtained as follows:
τ＝G(ν s ,ν a )ξ o (4)
wherein ,
where τ represents a control input matrix of the USV-UAV cooperative system; g (v) s ,ν a ) A control input gain matrix representing the USV-UAV co-system; zeta type toy o Control command matrix representing USV-UAV co-system, F r Steering engine gain function, T, representing USV u Propeller gain function, k, representing USV p Representing parameters dependent on the geometry of the blade and the air density c d Representing the resistance coefficient; |·| represents absolute value operations;
in the S3, a sensor fault model of the USV-UAV cooperative system is established as follows:
in the formula ,representing the sensor output of the unmanned ship USV when j=s, and the unmanned UAV when j=a; ρ η Representing the failure coefficient of the sensor, +.>Representing a fault paranoid coefficient of the sensor;
in S4, the reference track of the USV is obtained as follows:
in the formula ,xsv Represents the x-direction position coordinates of VS, y sv Represents the y-direction position coordinates of VS, ψ sv Indicating the heading angle of VS, u v Representing VSA forward speed; v v Represents the yaw rate of VS; r is (r) v A yaw rate representing VS;representing a derivative operation;
the real-time reference course trajectory of the UAV is obtained as follows:
in the formula ,xavi Representing the spatial plane x-direction position coordinates of the ith VA; y is avi Representing the spatial plane y-direction position coordinates of the ith VA; psi phi type avi Representing the spatial plane heading angle of the ith VA; zeta type di A formation distance representing the ith VA; lambda (lambda) di A formation direction angle representing the ith VA;
in the step S5 of the above-mentioned method,
the reference position signal of the USV is obtained as follows:
in the formula ,ψsd Representing the reference heading angle, x of the USV se Represents the horizontal coordinate difference between USV and VS, y se Representing the difference between USV and VS;
the reference attitude signal of the UAV is obtained as follows:
in the formula ：ψadi Representing a reference heading of an ith UAV; phi (phi) adi A reference roll angle representing an ith UAV; θ adi Representing a reference pitch angle of the ith UAV; x is x aei Representing the difference of the horizontal coordinates of the ith USV and the ith VA on the space plane; y is aei Representing the difference of the vertical coordinates of the ith USV and the ith VA on the space plane; c x Position controller representing ith UAV along x-axis；c y Representing a position controller of the ith UAV along the y-axis; c z Representing a position controller of the ith UAV along the z-axis;
in the step S6, the method for acquiring the adaptive controller of the USV-UAV cooperative system is as follows:
s61: according to the sensor fault model of the USV-UAV cooperative system, the reference position signal of the USV and the reference posture signal of the UAV acquire the position error and the posture error of the USV-UAV cooperative system as follows:
in the formula ,ψse Represents the heading difference of USV, l sx Inverse, l representing failure coefficient of x-coordinate in position sensor of USV sy Inverse, l representing the failure coefficient of the y-coordinate in a position sensor of the USV sψ Representing the inverse of the failure coefficient of the USV's yaw sensor,a paranoid fault coefficient representing the x-coordinate in a position sensor of USV,/I>A paranoid fault coefficient representing the y-coordinate in a position sensor of USV,/I>A yaw failure coefficient representing a yaw sensor of a USV, +.>X-coordinate output signal of position sensor representing USV,/v>Y-coordinate output signal of position sensor representing USV,/v>Bow representing USVOutputting a signal to a sensor; z aei Represents the vertical coordinate difference, ψ, of the ith UAV aei Representing a heading difference of an ith UAV; phi (phi) aei Representing the roll difference of the ith UAV; θ aei Representing pitch difference of the ith UAV; l (L) axi Reciprocal of failure coefficient representing x-coordinate in position sensor of ith UAV, l ayi Reciprocal of failure coefficient representing y-coordinate in position sensor of ith UAV, l azi Reciprocal of failure coefficient representing z-coordinate in position sensor of ith UAV, l aψi Representing the inverse of the failure coefficient of the yaw sensor of the ith UAV, l aφi Representing the inverse of the failure coefficient of the roll sensor of the ith UAV, l aθi Representing the inverse of the failure coefficient of the pitch sensor of the ith UAV; />A paranoid fault coefficient representing the x-coordinate in the position sensor of the ith UAV,/->A paranoid fault coefficient representing the y-coordinate in the position sensor of the ith UAV,/->A paranoid fault coefficient representing the z-coordinate in the position sensor of the ith UAV,a paranoid fault coefficient representing a yaw sensor of an ith UAV,/>A paranoid fault coefficient representing the roll sensor of the ith UAV,/for>A paranoid fault coefficient representing a pitch sensor of an ith UAV; />Representing position transfer of UAVsOutput signal of the x-coordinate of the sensor, +.>Output signal representing the x-coordinate of the position sensor of the UAV,/for>Output signal representing the y-coordinate of the position sensor of the UAV, +.>Output signal indicative of a yaw sensor of a UAV,/->Output signal representing roll sensor of UAV,/->An output signal representative of a pitch sensor of the UAV;
s62: obtaining a virtual control law to eliminate a position error and an attitude error of a USV-UAV cooperative system;
the virtual control law is obtained as follows:
the sensor fault self-adaptive compensation law is acquired to acquire an optimized virtual control law as follows:
in the formula ,αus Representing the virtual control law of the USV in the forward direction;k sψ ,k ap ,,/>all are positive design parameters; />Representing the straight line distance of the USV to the reference position; delta △ Position boundary parameters representing USV; />Representing intermediate substitution variables, a delta sx Representing a positive design parameter of the USV for the hyperbolic tangent function in the forward direction; alpha rs Representing a virtual control law of the USV in a bow-swing direction; alpha pai Representing the position virtual control law of UAVs, +.>Representing a virtual control law of a pose of the UAV; and (V) sψ Representing a positive design parameter of the USV for the hyperbolic tangent function in the yaw direction; />Representing positive design parameters of the UAV for the hyperbolic tangent function in the pose direction; />Representing a derivative operation; />An estimate of (-); />Representation->Is a derivative value of (a); p is p aei Representing a position error of the UAV; />Representing an attitude error of the UAV; p is p avi Representing a reference position of the UAV; />Representing a reference pose of the UAV;
γ x1 ,γ x2 ,γ ψ1 ,γ ψ2 ,γ p1 ,γ p2 ,σ x1 ,σ x2 ,σ ψ1 ,σ ψ2 ,σ p1 ,σ p2 ,/>all represent positive adaptive design parameters; l (L) sx Inverse, l representing failure coefficient of x-coordinate in position sensor of USV sψ Inverse of the failure coefficient of the yaw sensor, representing the USV, < >>Representing the inverse of the failure coefficient of the attitude sensor of the UAV, l api Inverse, pi, representing the failure coefficient of the position sensor of the UAV sx Derivative of the paranoid coefficient, pi, representing a USV position sensor fault sψ Derivative of the paranoid coefficient representing a USV yaw sensor failure, +.>Derivatives of the paranoid coefficients representing UAV attitude sensor faults; pi-shaped structure api Representing the inverse of the failure coefficient of the position sensor of the UAV; l (L) sx (0) Representation l sx Is set to an initial value of (1); l (L) sψ (0) Representation l sψ Is set to an initial value of (1); l (L) api (0) Representation l api Is set to an initial value of (1); />Representation->Is set to an initial value of (1); pi-shaped structure sx (0) Representing pi sx Is set to an initial value of (1); pi-shaped structure sψ (0) Representing pi sψ Is set to an initial value of (1); pi-shaped structure api (0) Representing pi api Is set to an initial value of (1); />Representation->Is set to an initial value of (1);
s63: acquiring a speed error of the USV-UAV cooperative system according to the optimized virtual control law so as to acquire a derivative of the speed error of the USV-UAV cooperative system;
the derivative of the speed error of the USV-UAV co-system is obtained as follows:
in the formula ,use Representing the speed error of the USV; r is (r) se Representing a yaw rate error of the USV; u (u) pei Representing a position velocity error of the UAV; u (u) pi Representing a position velocity of the UAV; alpha upi Representing a position virtual control law of the UAV;representing an attitude velocity error of the UAV; />Representing a gesture velocity of the UAV; />Representing a virtual control law of a pose of the UAV; m is m u Representing the hydrodynamic additional mass of the USV in the forward direction; f (f) u A nonlinear term representing the USV in the forward direction; t (T) u Propeller gain coefficient representing USV; d, d wu Representing the disturbance force experienced by the USV in the forward direction; />Representing alpha us Is a derivative of the dynamic surface signal; />Representing alpha us The derivative of the dynamic face error of (a); m is m r Representing the hydrodynamic additional mass of the USV in the yaw direction; f (f) r Representing the non-linear term of the USV in the yaw direction; f (F) r Gain factor of steering engine representing USV; d, d wr Representing the disturbance moment of the USV in the bow-swing direction; />Representing alpha rs Is a derivative of the dynamic surface signal; />Representing alpha rs The derivative of the dynamic face error of (a); f (f) pi A position uncertainty term representing the UAV;
R pi representing a position gain matrix of the UAV; d, d wxi Representing external interference force applied to the UAV in the x-axis direction;representing alpha upi Is a derivative of the dynamic surface signal; />Representing alpha upi The derivative of the dynamic face error of (a); />Representing a pose gain matrix of the UAV; />Representing an attitude disturbance moment of the UAV; />Representation->Is a derivative of the dynamic surface signal; m is m a Representing the quality of the UAV; (. Cndot. -1 Representing an inverse function operation;
s64: establishing event triggering rules of the USV-UAV cooperative system:
in the formula ,cw1 (t w ),c w2 (t w ) Representing a trigger threshold parameter that is set to be equal to or greater than a threshold value,a control input representing the trigger time of the cooperating body; />Representing a current trigger point in time of the collaborative entity within different channels; τ w A control input representing a collaborative volume; t is t w Representing control times of the cooperators in different channels; t represents a trigger time; />Representing a next trigger point in time of the collaborative entity within a different channel; fi represents the lift direction of the UAV; />A gesture variable representing the UAV; e, e w A difference between the control input indicating the trigger time and the control input indicating the non-trigger time;
in the formula ,c0 Representing a positive minimum trigger parameter, η e Representing a status error of the USV-UAV collaboration system;
therefore, it is possible to obtain,
in the formula ,λw1 and λw2 All represent trigger range defining parameters; c w1 (t w) and cw2 (t w ) All represent trigger threshold parameters;
s65: according to the event triggering rule of the USV-UAV cooperative system, the intermediate controller is acquired as follows:
in the formula ,ksu ,k sr Representing controller design parameters, k, of USV ap ,Representing controller design parameters of the UAV; k (k) un ,k rn Robust neural damping design parameters, k, representing USV pn ,/>Representing robust neural damping design parameters of the UAV; phi u ,Φ r Robust neural damping term, Φ, representing USV pi ,/>A robust neural damping term representing an ith UAV;
it is derived that the method comprises the steps of,
s66: the adaptive controller that obtains the USV-UAV co-system is as follows:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310067964.7A CN116047909B (en) | 2023-01-13 | 2023-01-13 | Unmanned plane-ship cooperative robust self-adaptive control method for maritime parallel search |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310067964.7A CN116047909B (en) | 2023-01-13 | 2023-01-13 | Unmanned plane-ship cooperative robust self-adaptive control method for maritime parallel search |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116047909A CN116047909A (en) | 2023-05-02 |
| CN116047909B true CN116047909B (en) | 2023-09-05 |
Family
ID=86125320
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310067964.7A Active CN116047909B (en) | 2023-01-13 | 2023-01-13 | Unmanned plane-ship cooperative robust self-adaptive control method for maritime parallel search |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116047909B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117055578A (en) * | 2023-09-21 | 2023-11-14 | 大连海事大学 | USV-UAV cooperative path tracking control method with low design complexity requirement |
| CN117193344B (en) * | 2023-09-21 | 2024-03-15 | 大连海事大学 | Synchronous guidance-based trigger control method for machine/ship collaborative multi-task event |
| CN117193345B (en) * | 2023-09-21 | 2024-03-26 | 大连海事大学 | Autonomous landing control method for machine-ship cooperative platform based on dynamic event triggering |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104709453A (en) * | 2015-01-19 | 2015-06-17 | 王显春 | Jet-propelled unmanned submarine |
| CN105292398A (en) * | 2015-11-10 | 2016-02-03 | 四方继保（武汉）软件有限公司 | Unmanned aerial vehicle mixed system carried by unmanned ship |
| CN107450568A (en) * | 2017-09-25 | 2017-12-08 | 武汉理工大学 | Ship entering and leaving port accessory system under group's unmanned plane collaboration |
| CN112033441A (en) * | 2020-09-11 | 2020-12-04 | 武汉大学 | Linkage formation integrity monitoring method used under BDS/MEMS combined navigation |
| WO2021018647A1 (en) * | 2019-07-26 | 2021-02-04 | Currenta Gmbh & Co. Ohg | Method and apparatus for monitoring quality and determining contamination of an area |
| WO2021022637A1 (en) * | 2019-08-06 | 2021-02-11 | 南京赛沃夫海洋科技有限公司 | Unmanned surface vehicle path planning method and system based on improved genetic algorithm |
| CN112612212A (en) * | 2020-12-30 | 2021-04-06 | 上海大学 | Heterogeneous multi-unmanned system formation and cooperative target driving-away method |
| WO2021078003A1 (en) * | 2019-10-22 | 2021-04-29 | 深圳市道通智能航空技术有限公司 | Obstacle avoidance method and device for unmanned vehicle, and unmanned vehicle |
| CN113419428A (en) * | 2021-07-14 | 2021-09-21 | 大连海事大学 | USV-UAV cooperative path tracking adaptive controller design method based on 3D mapping guidance |
| CN113961001A (en) * | 2021-08-27 | 2022-01-21 | 中国人民解放军军事科学院战争研究院 | RVO strategy and optimal control algorithm-based hybrid multi-agent collaborative path planning method |
| CN114488803A (en) * | 2022-01-19 | 2022-05-13 | 大连海事大学 | Unmanned ship/aircraft robust adaptive neural cooperative control method based on event triggering |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110658829B (en) * | 2019-10-30 | 2021-03-30 | 武汉理工大学 | Intelligent collision avoidance method for unmanned surface vehicle based on deep reinforcement learning |
-
2023
- 2023-01-13 CN CN202310067964.7A patent/CN116047909B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104709453A (en) * | 2015-01-19 | 2015-06-17 | 王显春 | Jet-propelled unmanned submarine |
| CN105292398A (en) * | 2015-11-10 | 2016-02-03 | 四方继保（武汉）软件有限公司 | Unmanned aerial vehicle mixed system carried by unmanned ship |
| CN107450568A (en) * | 2017-09-25 | 2017-12-08 | 武汉理工大学 | Ship entering and leaving port accessory system under group's unmanned plane collaboration |
| WO2021018647A1 (en) * | 2019-07-26 | 2021-02-04 | Currenta Gmbh & Co. Ohg | Method and apparatus for monitoring quality and determining contamination of an area |
| WO2021022637A1 (en) * | 2019-08-06 | 2021-02-11 | 南京赛沃夫海洋科技有限公司 | Unmanned surface vehicle path planning method and system based on improved genetic algorithm |
| WO2021078003A1 (en) * | 2019-10-22 | 2021-04-29 | 深圳市道通智能航空技术有限公司 | Obstacle avoidance method and device for unmanned vehicle, and unmanned vehicle |
| CN112033441A (en) * | 2020-09-11 | 2020-12-04 | 武汉大学 | Linkage formation integrity monitoring method used under BDS/MEMS combined navigation |
| CN112612212A (en) * | 2020-12-30 | 2021-04-06 | 上海大学 | Heterogeneous multi-unmanned system formation and cooperative target driving-away method |
| CN113419428A (en) * | 2021-07-14 | 2021-09-21 | 大连海事大学 | USV-UAV cooperative path tracking adaptive controller design method based on 3D mapping guidance |
| CN113961001A (en) * | 2021-08-27 | 2022-01-21 | 中国人民解放军军事科学院战争研究院 | RVO strategy and optimal control algorithm-based hybrid multi-agent collaborative path planning method |
| CN114488803A (en) * | 2022-01-19 | 2022-05-13 | 大连海事大学 | Unmanned ship/aircraft robust adaptive neural cooperative control method based on event triggering |
Non-Patent Citations (1)
| Title |
|---|
| Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision;JORGE PEÑA QUERALTA;IEEE;191617-191643 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116047909A (en) | 2023-05-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116047909B (en) | Unmanned plane-ship cooperative robust self-adaptive control method for maritime parallel search | |
| Li et al. | A novel cooperative design for USV–UAV systems: 3-D mapping guidance and adaptive fuzzy control | |
| CN108445892A (en) | A kind of drive lacking unmanned boat formation control device structure and design method | |
| CN113093804B (en) | Unmanned ship formation control method and control system based on inversion sliding mode control | |
| CN110308735A (en) | A kind of drive lacking UUV track following sliding-mode control for input delay | |
| CN105425812B (en) | Unmanned aerial vehicle automatic landing trajectory control method based on dual models | |
| CN113419428B (en) | Machine/ship cooperative path tracking controller design method based on 3D mapping guidance | |
| CN114488803A (en) | Unmanned ship/aircraft robust adaptive neural cooperative control method based on event triggering | |
| US11853082B1 (en) | Control of vehicle movement by application of geometric algebra and state and error estimation | |
| Li et al. | Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UAVs cooperative formation system for maritime parallel search | |
| CN111857165B (en) | Trajectory tracking control method of underwater vehicle | |
| CN111045440B (en) | Hypersonic aircraft nose-down section rapid rolling control method | |
| Wiig et al. | A 3D reactive collision avoidance algorithm for underactuated underwater vehicles | |
| CN117389312B (en) | Model-based anti-roll three-dimensional tracking control method for underwater vehicle | |
| CN112327926B (en) | Self-adaptive sliding mode control method for unmanned aerial vehicle formation | |
| CN113741433B (en) | Distributed formation method of unmanned ship on water surface | |
| CN111290278A (en) | Hypersonic aircraft robust attitude control method based on prediction sliding mode | |
| CN117193345B (en) | Autonomous landing control method for machine-ship cooperative platform based on dynamic event triggering | |
| CN114564015A (en) | Distributed formation control method for under-actuated unmanned ship in rejection environment | |
| CN113110538A (en) | Fixed-time fault-tolerant control method for carrier-based aircraft landing based on backstepping control | |
| CN111435253A (en) | Bounded output control for four-rotor aircraft | |
| CN115657713B (en) | Launching decision control method considering launching platform sinking and floating and shaking conditions | |
| CN116203840A (en) | Adaptive gain scheduling control method for reusable carrier | |
| CN113050679B (en) | Fully-distributed robust missile formation cooperative controller | |
| CN112947448B (en) | Unmanned ship cluster collaborative surrounding multi-target fuzzy controller structure and design method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |