CN108776432A - Network-based airfield runway detection robot forecast Control Algorithm - Google Patents
Network-based airfield runway detection robot forecast Control Algorithm Download PDFInfo
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Abstract
The invention discloses a kind of network-based airfield runway detection robot forecast Control Algorithms, the present invention establishes airfield runway detection robot kinematics model for a kind of airfield runway detection Robot Control System Based on Network, and it is converted controlled autoregressive used in LMS control and integrates gliding model, then it is robot independent single-input single-output linear model in the directions x and two, the directions y under global coordinate system by this model conversation, and robot is detected to airfield runway by improved GPC algorithms and carries out PREDICTIVE CONTROL.The present invention can effectively reduce in airfield runway detection robot kinematics by remote network control caused by network delay and noise jamming is influenced, improve computing speed and its robustness that airfield runway detects robot motion controller in a network environment.
Description
Technical field
The present invention relates to a kind of robot predicting control method more particularly to a kind of network-based airfield runway detection machines
Device people's forecast Control Algorithm, belongs to motion planning and robot control technical field under network environment.
Background technology
With the development of modern Aviation industry, airfield runway is got over as the Important Platform for taking off landing, safety problem
To be more taken seriously.Defect and foreign matter on airfield runway bring prodigious threat to takeoff and landing process.Therefore, airport is run
The safety detection in road is to ensuring that aircraft smoothly takes off landing with critically important realistic meaning.The present invention is in wireless network environment
Under, robot realization is detected by remote control airfield runway, the safety of runway is detected.The airfield runway detects machine
The movement of people receives sensor signal by network by remote control center and motion control signal is transferred to the robot,
The problems such as there are network random delay and packet losses causes the motion control performance of system to be affected, original control method
It cannot meet the requirements.
Since the GPC algorithm that 20th century Clarke et al. propose, since it is with good control effect
It with stronger robustness, is had obtained relatively broad application in actual production process, but control algolithm needs are asked online
Solve diophantus (Diophantine) equation, and need to the operation inverted of control matrix, thus online amount of calculation compared with
Greatly.For this disadvantage of GPC algorithm, lot of domestic and international scholar studies it.It proposes a series of
Improved method, it is therefore intended that calculation amount is reduced, wherein with implicit GPC algorithms with more.Although this method avoids solution
Diophantine equations, but it is there is still a need for inverting, and leads to not carry out quickly and effectively remote real_time control to robot.
Invention content
For the above-mentioned prior art, the technical problem to be solved in the present invention is to provide one kind can there are network delay and
The network-based airfield runway that the motor control problems of airfield runway detection robot are solved in the environment of interference detects machine
People's forecast Control Algorithm.
In order to solve the above technical problems, the present invention provides a kind of network-based airfield runway detection robot PREDICTIVE CONTROL
Method includes the following steps：
S1：Mecanum wheel (Mecanum wheel) the movement chassis that robot is detected according to airfield runway is moved
Modeling is learned, kinematics model is obtained；
S2：The kinematics model that airfield runway is detected to robot is converted into controlled autoregressive integrated moving average model；
S3：The controlled autoregressive integrated moving average model of airfield runway detection robot is converted respectively and obtains global seat
The model in the directions y under the model and global coordinate system in the lower directions x of mark system, and generalized predictive control is carried out respectively to it；
S4：Original controller performance index function is established using GPC algorithm；
S5：Performance index function is rebuild on the basis of original controller performance index function, obtains reconstruction
Performance index function；
S6：The controlled quentity controlled variable Δ V in the directions x is obtained according to the performance index function of reconstructionx(k) and the controlled quentity controlled variable Δ V in the directions yy
(k), by wireless network module by Δ Vx(k) and Δ Vy(k) it sends airfield runway to from host computer and detects robot, wherein：
A kind of network-based airfield runway of the present invention detects robot forecast Control Algorithm, further includes：
The kinematics model of airfield runway detection robot meets in 1.S1：
Wherein vxThe robot directions x speed, v under global coordinate system are detected for airfield runwayyMachine is detected for airfield runway
The people's directions y speed under global coordinate system, ω are the rotational angular velocity that airfield runway detects robot, and θ is under global coordinate system
Airfield runway detects the angle between robot and x-axis, and R represents the radius of Mecanum wheel, l1For each Mecanum wheel with
The distance of x-axis, l under individual coordinate system2For each Mecanum wheel under individual coordinate system at a distance from y-axis, v1,v2,v3,v4Respectively
The rotating speed of the motor of four Mecanum wheels in order to control.
Controlled autoregressive integrated moving average model meets in 2.S2：
Wherein z-1That a step steps back operator, Δ x, Δ y be respectively field runway detection robot in time T in world coordinates
Displacement variable on the lower direction x, y of system, d is delay, z-dFor the delay factor of introducing.
The controlled autoregressive integrated moving average model in the directions x meets in 3.S3：
(1-z-1) x=Tz-dvx(k-1)
The controlled autoregressive integrated moving average model in the directions y meets respectively：
(1-z-1) y=Tz-dvy(k-1)。
Performance index function meets in 4.S4：
Wherein E is mathematic expectaion, Δ u increments, and Δ u (t+j)=0, j=N in order to controlu,...,N1, indicate in NuAfter step
Controlled quentity controlled variable no longer changes.N1、N2Respectively minimum, maximum predicted time domain length, NuTime domain length in order to control, λ is weighted in order to control is
Number, y (k+j) are the output predicted value of system j steps, yr(k+j) it is the desired value of object output.
The performance index function rebuild in 5.S5 meets：
Wherein P is maximum predicted step number, giIt is i before device step response sampled values.
The controlled quentity controlled variable Δ V in the directions x is obtained according to the performance index function of reconstruction described in 6.S6x(k) and the control in the directions y
Measure Δ Vy(k) it is specially：
As object function and convert the performance index function of reconstruction to vector form, i.e.,：
J=E { [Y (k+1)-Yr(k+1)]T[Y(k+1)-Yr(k+1)]+λΔU(k)TGΔU(k)}
Controlled quentity controlled variable Δ u (k) is obtained by above formula, the controlled quentity controlled variable Δ V in the directions x is obtained according to Δ u (k)x(k) and the directions y
Controlled quentity controlled variable Δ Vy(k), wherein Δ u (k) meets：
Δ u (k)=qT[Yr(k+1)-E(z-1)Δu(k-1)-S(z-1)y(k)]
Beneficial effects of the present invention：The present invention is by introducing traditional generalized predictive control (Generalized
Predictive Control, GPC) it algorithm and makes improvements, to solve under network delay and noise jamming environment
Airfield runway detects the long-range motor control problems of robot.This method can effectively reduce airfield runway detection robot motion
In the process by remote network control caused by network delay and noise jamming influenced, improve airfield runway detection robot
The computing speed and its robustness of motion controller in a network environment.Compared with prior art, the present invention considers controlled
Object is under network-control environment, and network delay and noise jamming influence can be resolved so that airfield runway detects robot
Obtain stable motion control signal.Relative to traditional GPC controllers, heretofore described improved GPC controllers have knot
The features such as structure is simple, control algolithm computing speed is fast, strong robustness.
Description of the drawings
Fig. 1 is that the present invention is based on the schematic diagrams that the airfield runway of network detects robot forecast Control Algorithm.
Fig. 2 is the relational graph that airfield runway detects robot body coordinate system and global coordinate system.
Fig. 3 is the schematic diagram that airfield runway detects robot motion's bobbin movement model.
Specific implementation mode
Embodiments of the present invention are described with reference to the accompanying drawings of the specification.
The present invention is devised detects Robot Control System Based on Network for a kind of airfield runway, establishes airfield runway detection
Robot kinematics' model, and converted controlled autoregressive integral sliding (Controlled used in LMS control
Autoregressive Integrated Moving Average Model, CARIMA) model, be then by this model conversation
Robot independent single-input single-output linear model in the directions x and two, the directions y under global coordinate system, and by improved
GPC algorithms carry out PREDICTIVE CONTROL to airfield runway detection robot.
As shown in Figure 1, the present invention devises a kind of network-based airfield runway detection robot forecast Control Algorithm, packet
Include following steps：
Airfield runway detects robot in the case where being interfered there are outside noise ξ (k), by sensor by robot
Motion state causes forward path delay τ to remote control center by transmission of network due to network transmissionca, long-range to control
Center processed obtains control signal u (k) by operation, control signal is sent to driver again by network, also due to network
The reason of transmission, therefore generate backward channel delay τsc, remember that total delay is d=τsc+τca, last driver driving Mecanum
Wheel makes airfield runway detection robot work.
The movement chassis of airfield runway detection robot is made of four Mecanum wheels, and machine is detected according to airfield runway
People's Mecanum wheel moves chassis and carries out Kinematic Model.
As shown in Fig. 2, establishing robot body coordinate system on robot chassis, the overall situation is environmentally established in robot motion
Coordinate system, coordinate system are rectangular coordinate system, the angle theta under global coordinate system between robot and x-axis, and robot body is sat
Mark system and the transformational relation of global seat system are：
As shown in figure 3, the robot directions x speed under global coordinate system is vx, the directions y speed is vy, angle of rotation speed
Degree is ω, and the radius of Mecanum wheel is R, and the distance between each Mecanum wheel and individual coordinate system x-axis are l1, with y-axis
The distance between be l2, the rotating speed of four Mecanum wheels in chassis is respectively v1,v2,v3,v4, airfield runway detection robot motion
Learning equation is：
Using model by parameter [vx vy ω]TConvert the motor speed [v of four Mecanum wheels in order to control1 v2 v3
v4]T, realize the motion control of mobile robot.
Controlled autoregressive integral sliding average (Controlled used in LMS control is used in GPC
Autoregressive Integrated Moving Average Model, CARIMA) model, it is described as follows：
A(z-1) y (k)=B (z-1)u(k-1)+C(z-1)ξ(k)/Δ
Wherein：
Δ=1-z-1
z-1It is that a step steps back operator, y (k) is the reality output of system, and u (k) is system control amount, and ξ (k) is noise sequence
Row, { ai},{biAnd { ciIt is A, B and C respectively corresponding multinomial coefficient, na、nbAnd ncIt is corresponding respective order,
Δ is difference operator.
In airfield runway detects robot kinematics, vx、vyThe position of robot car in the plane can be controlled,
ω control robot car just facing towards.Since the robot can be put down with any speed residing for robot in any angle
It is moved on face, ω is unrelated with the location variation of robot, can be implemented by individual control strategy, therefore the calculating of ω can be with
vx,vyOperation independent is separated, the specific method is as follows：
The CARIMA models in robot directions x and the directions y under global coordinate system are obtained by robot kinematics' principle：
(1-z-1) x=Tz-dvx(k-1)
(1-z-1) y=Tz-dvy(k-1)
Wherein, T is that robot shifts up time used, z in x, the side y-dFor the delay factor of introducing.
Introduce Diophantine equation：
1=Rj(z-1)A(z-1)Δ+z-jSj(z-1)
Rj(z-1)B(z-1)=Gj(z-1)+z-jEj(z-1)
Wherein：
Gj(z-1)=g0+g1z-1+…+gjz-j+1
DegA=na
DegB=nb
Traditional controller performance index function is
Wherein E is mathematic expectaion, Δ u increments, and Δ u (k+j)=0, j=N in order to controlu,...,N1, indicate in NuAfter step
Controlled quentity controlled variable no longer changes.N1、N2Respectively minimum, maximum predicted time domain length, NuTime domain length in order to control, λ is weighted in order to control is
Number, y (k+j) are the output predicted value of system j steps, yr(k+j) it is the desired value of object output.
It is improved on the basis of traditional controller performance index function, performance index function is rebuild：
Wherein, P is maximum predicted step number, j=1 ..., P, i=0 ..., j-1, and Δ u increments in order to control, y (k+j) is to be
The output predicted value for j steps of uniting, yrIt is given value, λ weighting coefficients in order to control, giIt is i before device step response sampled values, Δ u
Increment in order to control.
Write object function as vector form：
J=E { [Y (k+1)-Yr(k+1)]T[Y(k+1)-Yr(k+1)]+λΔU(k)TGΔU(k)}
Wherein：
Y (k+1)=[y (k+1), y (k+2) ..., y (k+P)]T
Yr(k+1)=[yr(k+1),yr(k+2),...,yr(k+P)]T
Δ U (k)=[Δ u (k), Δ u (k+1) ..., Δ u (k+P-1)]T
Ask local derviation that can obtain at Δ U object function：
It is 0 to enable above formula, and it is as follows can to obtain corresponding control law：
Δ U (k)=(G+ λ I)-1[Yr(k+1)-E(z-1)Δu(k-1)-S(z-1)y(k)]
Therefore, new control rate is：
Δ u (k)=qT[Yr(k+1)-E(z-1)Δu(k-1)-S(z-1)y(k)]
Wherein：
qT=(1,0 ..., 0) (G+ λ I)-1
E(z-1)=[E1(z-1),E2(z-1),…,Ep(z-1)]T
S(z-1)=[S1(z-1),S2(z-1),…,Sp(z-1)]T
Robot required control rate on the directions x and the directions y can be obtained according to Δ u (k).
In remote control center host computer, robot is sent to by signal is controlled by network, realizes robot in net
Trajectory Tracking Control movement is carried out under network environment.
The specific embodiment of the invention further includes：
The movement chassis that robot is detected according to airfield runway is made of four Mecanum wheels, establishes the fortune on movement chassis
Dynamic to learn model, the specific method is as follows：
Robot body coordinate system is established on robot chassis, global coordinate system is environmentally established in robot motion；
If vxFor the movement velocity in robot directions x under global coordinate system, vyFor robot under global coordinate system the side y
To movement velocity, ω is the rotational angular velocity of robot, and R represents the radius of Mecanum wheel, and θ is machine under global coordinate system
Angle between people and x-axis, l1For the distance between the x-axis of each Mecanum wheel and individual coordinate system, l2It is received for each Mike
The distance between the y-axis of nurse wheel and individual coordinate system, v1,v2,v3,v4The motor speed of four Mecanum wheels in order to control, airport
Runway detection Robot kinematics equations are：
The model can be by parameter [vx vy ω]TConvert the motor speed [v of four Mecanum wheels in order to control1 v2 v3
v4]T, motion control can be carried out to mobile robot based on this model.
During moveable robot movement, vx、vyThe position of robot car in the plane, ω control machines can be controlled
Device people's trolley just facing towards.
Since mobile robot can be in any angle to move in any speed plane residing for robot, ω and machine
The location variation of people is unrelated, and can be implemented by individual control strategy, therefore the calculating of ω can be with vx,vySeparate independent fortune
It calculates.
Establish the CARIMA models in robot directions x and the directions y under global coordinate system：
(1-z-1) x=Tz-dvx(k-1)
(1-z-1) y=Tz-dvy(k-1)
Wherein z-1It is that a step steps back operator, T is that robot shifts up the time used in x, the side y, and d is delay, z-dFor
The delay factor of introducing carries out GPC controls to both direction respectively.
Performance index function is rebuild on the basis of traditional GPC controller performances target function.
Further, robot required control rate on the directions x and the directions y under global coordinate system is found out respectively.
In remote control center host computer, robot sensor feedback signal is received by network, is obtained by operation
Motion planning and robot control amount is obtained, robot is sent to by signal is controlled by network.
Embodiments of the present invention are elaborated above in conjunction with attached drawing, but the present invention is not limited to above-mentioned implementations
Mode within the knowledge of a person skilled in the art can also be without departing from the purpose of the present invention
It makes a variety of changes.
Claims (7)
1. a kind of network-based airfield runway detection robot forecast Control Algorithm, it is characterised in that：Include the following steps：
S1：The Mecanum wheel movement chassis that robot is detected according to airfield runway carries out Kinematic Model, obtains kinematics mould
Type；
S2：The kinematics model that airfield runway is detected to robot is converted into controlled autoregressive integrated moving average model；
S3：The controlled autoregressive integrated moving average model of airfield runway detection robot is converted respectively and obtains global coordinate system
The model in the directions y under the model and global coordinate system in the directions lower x, and generalized predictive control is carried out respectively to it；
S4：Original controller performance index function is established using GPC algorithm；
S5：Performance index function is rebuild on the basis of original controller performance index function, obtains the performance of reconstruction
Target function；
S6：The controlled quentity controlled variable Δ V in the directions x is obtained according to the performance index function of reconstructionx(k) and the controlled quentity controlled variable Δ V in the directions yy(k), lead to
Wireless network module is crossed by Δ Vx(k) and Δ Vy(k) it sends airfield runway to from host computer and detects robot, wherein：
2. a kind of network-based airfield runway detection robot forecast Control Algorithm according to claim 1, feature
It is：The kinematics model of airfield runway detection robot described in S1 meets：
Wherein vxThe robot directions x speed, v under global coordinate system are detected for airfield runwayyRobot is detected for airfield runway to exist
The directions y speed under global coordinate system, ω are the rotational angular velocity that airfield runway detects robot, and θ is airport under global coordinate system
Angle between runway detection robot and x-axis, R represent the radius of Mecanum wheel, l1For each Mecanum wheel and individual
The distance of x-axis, l under coordinate system2For each Mecanum wheel under individual coordinate system at a distance from y-axis, v1,v2,v3,v4Respectively control
Make the rotating speed of the motor of four Mecanum wheels.
3. a kind of network-based airfield runway detection robot forecast Control Algorithm according to claim 1, feature
It is：Controlled autoregressive integrated moving average model described in S2 meets：
Wherein z-1That a step steps back operator, Δ x, Δ y be respectively field runway detection robot in time T under global coordinate system
X, the displacement variable on the directions y, d are delay, z-dFor the delay factor of introducing.
4. a kind of network-based airfield runway detection robot forecast Control Algorithm according to claim 1, feature
It is：The controlled autoregressive integrated moving average model in the directions x described in S3 meets：
(1-z-1) x=Tz-dvx(k-1)
The controlled autoregressive integrated moving average model in the directions y described in S3 meets respectively：
(1-z-1) y=Tz-dvy(k-1)。
5. a kind of network-based airfield runway detection robot forecast Control Algorithm according to claim 1, feature
It is：Performance index function described in S4 meets：
Wherein E is mathematic expectaion, Δ u increments, and Δ u (t+j)=0, j=N in order to controlu,...,N1, indicate in NuIt is controlled after step
Amount no longer changes.N1、N2Respectively minimum, maximum predicted time domain length, NuTime domain length in order to control, λ weighting coefficients in order to control, y
(k+j) it is the output predicted value of system j steps, yr(k+j) it is the desired value of object output.
6. a kind of network-based airfield runway detection robot forecast Control Algorithm according to claim 1, feature
It is：The performance index function of reconstruction described in S5 meets：
Wherein P is maximum predicted step number, giIt is i before device step response sampled values.
7. a kind of network-based airfield runway detection robot forecast Control Algorithm according to claim 1, feature
It is：The controlled quentity controlled variable Δ V in the directions x is obtained according to the performance index function of reconstruction described in S6x(k) and the controlled quentity controlled variable Δ V in the directions yy
(k) it is specially：
As object function and convert the performance index function of reconstruction to vector form, i.e.,：
J=E { [Y (k+1)-Yr(k+1)]T[Y(k+1)-Yr(k+1)]+λΔU(k)TGΔU(k)}
Controlled quentity controlled variable Δ u (k) is obtained by above formula, the controlled quentity controlled variable Δ V in the directions x is obtained according to Δ u (k)x(k) and the control in the directions y
Measure Δ Vy(k), wherein Δ u (k) meets：
Δ u (k)=qT[Yr(k+1)-E(z-1)Δu(k-1)-S(z-1)y(k)]
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| US11565781B1 (en) | 2021-10-09 | 2023-01-31 | CIMC Offshore Co., Ltd | Hybrid-driven mooring chain cleaning and structural inspection underwater robot and working method thereof |
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