CN115686002A - Method for tracking and controlling path of unmanned surface vehicle under complex sea area - Google Patents
Method for tracking and controlling path of unmanned surface vehicle under complex sea area Download PDFInfo
- Publication number
- CN115686002A CN115686002A CN202211304752.8A CN202211304752A CN115686002A CN 115686002 A CN115686002 A CN 115686002A CN 202211304752 A CN202211304752 A CN 202211304752A CN 115686002 A CN115686002 A CN 115686002A
- Authority
- CN
- China
- Prior art keywords
- unmanned
- unmanned ship
- surface vehicle
- path
- motion state
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000005457 optimization Methods 0.000 claims abstract description 36
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 21
- 238000005096 rolling process Methods 0.000 claims abstract description 9
- 235000012907 honey Nutrition 0.000 claims description 35
- 230000035945 sensitivity Effects 0.000 claims description 12
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000006073 displacement reaction Methods 0.000 claims description 6
- 239000003016 pheromone Substances 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 230000036571 hydration Effects 0.000 claims description 3
- 238000006703 hydration reaction Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- NRNCYVBFPDDJNE-UHFFFAOYSA-N pemoline Chemical compound O1C(N)=NC(=O)C1C1=CC=CC=C1 NRNCYVBFPDDJNE-UHFFFAOYSA-N 0.000 claims description 3
- 238000010845 search algorithm Methods 0.000 claims description 3
- 239000011248 coating agent Substances 0.000 claims description 2
- 238000000576 coating method Methods 0.000 claims description 2
- 238000002360 preparation method Methods 0.000 claims description 2
- 230000003071 parasitic effect Effects 0.000 claims 1
- 230000008859 change Effects 0.000 abstract description 6
- 230000001186 cumulative effect Effects 0.000 abstract description 4
- 238000005259 measurement Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 241000257303 Hymenoptera Species 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 238000013507 mapping Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000012876 topography Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 239000003659 bee venom Substances 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to a path tracking control method for an unmanned surface vehicle under a complex sea area, which outputs real-time unmanned vehicle motion state information through Beidou second generation, integrated navigation and AIS equipment. The method comprises the steps of receiving the motion state information of the unmanned ship, predicting the motion state of the unmanned ship at a future moment, feeding errors between predicted values and reference values back to a rolling optimization part, constructing an optimization function of unmanned ship path deviation, solving an optimal solution of the optimization function under the constraint conditions of state variables and control input, obtaining an optimal command rudder angle by adopting a model prediction control algorithm and based on the constraint model prediction control of improved artificial bee colony optimization, and outputting the optimal command rudder angle to an unmanned ship motion control system. The model prediction control algorithm based on the improved artificial bee colony is adopted to realize that the unmanned ship can track the reference path with high precision under the time-varying interference of the storm flow, the rudder angle change is small and smooth, the cumulative influence of uncertainty is effectively reduced, and the unmanned ship path tracking precision under the complex sea condition is improved.
Description
Technical Field
The invention belongs to the technical field of unmanned surface unmanned ship control, and particularly relates to a method for tracking and controlling a path of an unmanned surface ship in a complex sea area.
Background
The unmanned ship is a general water surface intelligent task platform which is increasingly widely applied, and has the characteristics of small volume, low cost, flexibility, high navigational speed, intellectualization, small radar reflection area, no casualties and the like. The unmanned surface vehicle shown in fig. 1 plays an increasingly important role in the fields of marine environment measurement, submarine topography measurement, marine resource development and the like, and has a wide application prospect. The path tracking control is one of key technologies for realizing accurate navigation and executing some complex line-following measurement and mapping tasks of the unmanned surface vehicle, and the high-precision path tracking is the key for guaranteeing the accuracy of measurement and mapping of marine topography and landform. The path tracking is a difficult and difficult problem which needs to be solved in the application of the unmanned surface vehicle in the field of ocean surveying and mapping, so that the research on the path tracking control of the unmanned surface vehicle has important significance for improving the automation and intelligent level of the unmanned surface vehicle and promoting the marketization of the unmanned surface vehicle.
When the unmanned surface vehicle is sailed under a complex sea condition, the unmanned surface vehicle is disturbed by the force of ocean environments such as wind, waves and currents, and model parameters of the unmanned surface vehicle can present uncertainty, so that a control method with high robustness is needed to ensure the stability of a control system of the unmanned surface vehicle and the accuracy of the motion of the unmanned surface vehicle.
The motion of the unmanned surface vehicle under the mixed influence of wind, wave and flow has strong nonlinearity, strong coupling and larger uncertainty, and high-frequency interference in the marine environment can cause the derivation of state variables to have larger errors or can not be derived, so that the problem is more obvious when the order of the system is higher.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for tracking and controlling the path of an unmanned surface vehicle under a complex sea area, which adopts an online optimized model prediction algorithm to solve the interference of wind, wave and flow in the sea environment under complex sea conditions on the unmanned surface vehicle; meanwhile, a constraint model controller based on improved artificial bee colony optimization is adopted, so that the unmanned surface vehicle can track the reference path with high precision under the time-varying interference of storm flow, the rudder angle change is small and smooth, the cumulative influence of uncertainty is effectively reduced, and the unmanned surface vehicle path tracking precision is improved.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
a method for tracking and controlling a path of an unmanned surface vehicle under a complex sea area comprises the following steps:
step 1, outputting real-time unmanned ship motion state information through Beidou second generation, combined navigation and AIS equipment carried by an unmanned ship;
step 2, considering wind, wave and flow interference and response characteristics of a steering engine, constructing a model prediction controller to receive the unmanned ship motion state information and predict the unmanned ship motion state at a future moment;
step 3, feeding back errors of the predicted values and the reference values to a rolling optimization part, and constructing an optimization function of the unmanned ship path deviation;
step 4, solving an optimal solution for the optimization function by using an improved artificial bee colony algorithm under the constraint conditions of state variables and control input to obtain an optimal command rudder angle, and outputting the optimal command rudder angle to the unmanned ship motion control system;
and 5, the unmanned ship motion control system feeds the actual value back to the controller, and returns to the step 3 for rolling optimization.
And in the step 1, the unmanned ship motion state information comprises position information, navigational speed information and heading information.
Moreover, the model predictive controller constructed in the step 2 is:
wherein, in the motion coordinate of the unmanned surface vehicle, the X axis is specified to point to the true north, and the included angle between the central line of the head and the tail of the ship and the X axis, namely the ship heading angle is used
And (4) showing.
And V c Respectively the flow direction and the flow velocity of the flow u r For the water advance speed, v r For transverse velocity to water, for hydration velocity
The components of the movement speed of the unmanned surface vehicle on the oxyz coordinate system along the x axis and the y axis are u and V respectively, u is the ground advance speed, V is the ground transverse moving speed, the angular speed of the bow rotating around the z axis is r, and the ground resultant speed is V = (u is the speed of the unmanned surface vehicle on the ground) 2 +v 2 ) 1/2 The drift angle β = arctan (v/u), δ is the rudder angle. Delta. For the preparation of a coating r To command rudder angle, K E For steering engine control gain, T E Is the steering engine time constant, m is the water surface unmanned ship mass, m x And m y For additional mass, X H 、Y H And N H For viscous fluid-like dynamics acting on the hull, X P 、Y P And N P Is propeller force, X W 、Y W And N W Is the wind power, X Wave 、Y Wave And N Wave For wave power, I ZZ Moment of inertia about vertical axis for surface unmanned craft, J ZZ To add moment of inertia, X R 、Y R And N R Is rudder force, t R Is the fraction of rudder resistance reduced, a H Is the ratio of the hull additional transverse force to the rudder transverse force, x, caused by steering H The distance from the transverse force action center of the steering induced hull to the gravity center of the unmanned surface vehicle F N Is a rudder positive pressure.
Moreover, the specific implementation method for predicting the unmanned ship motion state at the future moment in the step 2 is as follows:
wherein,
for the unmanned boat motion state at the future k +1 moment,
the unmanned ship motion state at the moment k is obtained. T is a unit of c Is the prediction sampling time, is the time interval between two consecutive prediction values,
and
respectively discretizing each differential equation in the model predictive controller;
path tracking taking into account only lateral displacement, from the current k instant to the future N P The output of each moment is predicted:
moreover, the specific implementation method of step 3 is as follows: according to the prediction result and the reference transverse displacement y d Calculating the path prediction error
Wherein j =1,2, \8230, N P And constructing an optimization function according to the prediction error:
where Q is a weight matrix.
Moreover, the improved artificial bee colony algorithm in the step 4 is as follows: the method adopts a sensitivity-based free search algorithm, replaces a roulette mode with a mode of matching sensitivity and pheromone to select the honey source, and comprises the following steps:
step 4.1, calculating the fitness value f (X) of each honey source of N;
step 4.2, calculating pheromone nf (i) of the ith honey source:
4.3, randomly generating sensitivities S (i) -U (0, 1) of the ith follower bee;
step 4.4, finding out a bee source matched with the sensitivity of the ith following bee: finding out i randomly, satisfying nf (i) less than or equal to S (i)
Meanwhile, an improved strategy is adopted to generate a new honey source to replace the worst honey source: in each generation of circulation, the worst honey source is found, and the corresponding position is X b The new honey source position after the improved strategy is quoted is
The j-th dimension of the new location
Comprises the following steps:
wherein X jL As new honey source X j At the L-th dimension of X jH For, rand is honey source X j At the H-th dimension of X bj As new honey source X b The j-th dimension position.
Moreover, the method for calculating the optimal solution in step 4 comprises: by solving for QP quadratic form or within a constraint of delta min ≤δ≤δ max Equation of solution
Calculating an optimal command rudder angle:
the invention has the advantages and positive effects that:
1. the invention adopts an online optimized model prediction algorithm to solve the interference of wind, wave and flow in the ocean environment under complex sea conditions on the unmanned ship. By adopting the restraint model controller based on the optimization of the improved artificial bee colony, the unmanned surface vehicle can track the reference path with high precision under the time-varying interference of the storm flow, and the rudder angle change is small and smooth, thereby effectively reducing the cumulative influence of uncertainty and improving the path tracking precision of the unmanned surface vehicle.
2. The unmanned ship motion control system outputs real-time unmanned ship motion state information through Beidou second generation and combined navigation carried by the unmanned ship and AIS equipment, a model prediction controller is constructed to receive the motion state information and predict the unmanned ship motion state at a future moment, errors of predicted values and reference values are fed back to a rolling optimization part, an optimization function of unmanned ship path deviation is constructed, and finally, the optimization function is solved under the constraint conditions of state variables and control input, so that an optimal command rudder angle is obtained and output to the unmanned ship motion control system. And at the next moment, the unmanned ship motion control system continuously feeds the actual value back to the controller for rolling optimization, and the like, so that the high-precision path tracking control effect under the complex sea condition is realized. By adopting the method, the rudder angle change is small and smooth, the cumulative influence of uncertainty is effectively reduced, and the unmanned ship path tracking precision is improved.
Drawings
FIG. 1 is an ocean surveying type surface unmanned boat;
FIG. 2 illustrates the plane position and motion parameters of the unmanned surface vehicle under the interference of ocean currents;
FIG. 3 is a block diagram of a path tracking controller of the present invention;
FIG. 4 is a graph illustrating the effect of the straight path tracking path of the present invention;
FIG. 5 is a graph illustrating the effect of the curve path tracing path according to the present invention;
Detailed Description
The present invention is further described in detail below with reference to the accompanying drawings.
A method for tracking and controlling the path of an unmanned surface vehicle under a complex sea area is shown in figure 3 and comprises the following steps:
step 1, outputting real-time unmanned ship motion state information through Beidou second generation, combined navigation and AIS equipment carried by the unmanned ship. The unmanned ship motion state information comprises position information, navigation speed information and course information.
And 2, taking wind, wave and flow interference and steering engine response characteristics into consideration, and constructing a model prediction controller to receive the unmanned ship motion state information and predict the unmanned ship motion state at a future moment.
The model predictive controller considering wind, wave, flow interference and steering engine response characteristics is as follows:
wherein, in the motion coordinate of the unmanned surface vehicle, the X axis is specified to point to the true north, and the included angle between the central line of the head and the tail of the ship and the X axis, namely the ship heading angle is used
And (4) showing.
And V c Respectively the flow direction and the flow velocity of the flow u r For the advancing speed of water, v r For transverse velocity to water, for hydration velocity
The components of the movement speed of the unmanned surface vehicle on the oxyz coordinate system along the x axis and the y axis are u and V respectively, u is the ground advance speed, V is the ground transverse moving speed, the angular speed of the bow rotating around the z axis is r, and the ground resultant speed is V = (u is the speed of the unmanned surface vehicle on the ground) 2 +v 2 ) 1/2 The drift angle β = arctan (v/u), δ is the rudder angle. Delta r To command rudder angle, K E For steering engine control gain, T E Is the steering engine time constant, m is the water surface unmanned ship mass, m x And m y For additional mass, X H 、Y H And N H For viscous type hydrodynamics (moments), X, acting on the hull P 、Y P And N P Is propeller force (moment), X W 、Y W And N W Is the wind force (moment), X Wave 、Y Wave And N Wave Is wave force (moment), I ZZ Moment of inertia about vertical axis for surface unmanned craft, J ZZ To add moment of inertia, X R 、Y R And N R Rudder force (moment):
t R is the fraction of rudder resistance reduced, a H Is the ratio of the hull additional transverse force to the rudder transverse force, x, caused by steering H The distance from the transverse force action center of the steering induced hull to the gravity center of the unmanned surface vehicle F N Is a rudder positive pressure.
The specific implementation method for predicting the unmanned ship motion state at the future moment comprises the following steps:
wherein,
for the unmanned ship motion state at the future k +1 moment,
the unmanned ship motion state at the moment k is obtained. T is a unit of c Is the prediction sampling time, is the time interval between two consecutive prediction values,
and
respectively discretizing each differential equation in the model predictive controller;
path tracking can be achieved by only considering lateral displacement. From the current k time to the future N P The output of each moment is predicted:
step 3, feeding back the error between the predicted value and the reference value to a rolling optimization part, and constructing an optimization function of the unmanned ship path deviationAnd (4) counting. Based on the prediction result and the reference lateral displacement y d Calculating the path prediction error
Wherein j =1,2, \8230, N P And constructing an optimization function according to the prediction error:
where Q is a weight matrix. By solving for QP quadratic form or within a constraint of delta min ≤δ≤δ max Equation of solution
An optimal control law can be calculated. Because MPC is on-line calculative by computer, all intelligence on-line calculates control law through optimizing each step, and can not directly obtain analytic expression of control law. And solving a plurality of interference terms contained in the optimization function by using an improved artificial bee colony algorithm.
And 4, solving an optimal solution for the optimization function by using an improved artificial bee colony algorithm under the constraint condition of state variables and control input to obtain an optimal command rudder angle, and outputting the optimal command rudder angle to the unmanned ship motion control system.
In the ABC optimization algorithm, the population comprises bee collecting, observation and reconnaissance bees, the scale is 2 × SN, and the bee collecting and observation bees are SN. Set in m-dimensional space, the ith honey source position can be recorded as X i ＝(x i1 ,x i2 ,...,x im ) Each bee source represents an optimized solution to the optimization function.
In the process of searching the optimal honey source by the ABC optimization algorithm, the bee collecting and observing can be started according to v under the inspiration of the particle swarm optimization algorithm ij ＝x ij +r 1 (x ij -x kj )+r 2 (x best,j -x ij ) And searching a new adjacent honey source.
Wherein k, j is a randomly generated integer, k belongs to 1,2, SN, k is not equal to i, j belongs to 1,2, m, r 1 ∈[-1,1]Is also a random number, r 2 ∈[0,1.5]Is also a random number, x best,j The jth element of the globally optimal solution.
When the artificial bee colony solves the constraint quadratic programming for the first time, the overall optimal initial solution x best Selecting from randomly generated solutions, when not the first time, a globally optimal initial solution x best Get last control input sequence
The remaining m-1 control input components are used as the global optimal initial value of the artificial bee colony, namely
This will greatly improve the solution efficiency of the artificial bee colony algorithm.
The artificial bee colony algorithm has better function optimization effect, but the algorithm is easy to fall into local optimization due to the adoption of a roulette selection mode, and in the iterative process, the worst solution of each generation can participate in the generation of a new solution, so that the convergence speed of the algorithm is influenced. To this end, the present invention improves on the selection strategy, the worst solution alternative [9].
In the ABC algorithm, when honey sources are selected by following bees, in the ABC algorithm, when the honey sources are selected by following bees, a roulette mode is adopted, which is a selection mode based on a greedy strategy, so that the diversity of the population is reduced, and the phenomena of early convergence and early stagnation are caused in an optimization function. The free search algorithm based on sensitivity is adopted, and the method of matching sensitivity with pheromone replaces the roulette method to select the honey source. The specific process is as follows:
step 4.1, calculating the fitness value f (X) of each honey source;
step 4.2, calculating pheromone nf (i) of the ith honey source:
4.3, randomly generating sensitivities S (i) -U (0, 1) of the ith follower bee;
step 4.4, finding out a bee source matched with the sensitivity of the ith following bee: and finding out i randomly, and satisfying that nf (i) is less than or equal to S (i).
In the iteration process of the ABC algorithm, the leading bees and the following bee venom may depend on the worst honey source of the current generation to perform cross operation according to nf (i) to generate a new honey source, but the worst honey source is almost impossible to contribute to the required optimal result, so that the convergence speed of the algorithm is influenced to a certain extent. Here, an improvement strategy is adopted to generate a new honey source to replace the worst honey source, and the specific mode is as follows:
in each generation of circulation, the worst honey source is found, and the corresponding position is X b The new honey source position after the improved strategy is quoted as
The j-th dimension of the new location
Comprises the following steps:
and if the honey source corresponding to the new position is better, replacing the original honey source position with the honey source. If the optimal solution obtained by the artificial bee colony is x best Then, the optimal solution of the optimization function is:
according to the basic principle of model prediction control, the first component of the optimal solution sequence acts on the unmanned ship, at the next sampling moment, the state variable is measured again, the optimization problem described by the optimization function is refreshed, and the solution is carried out again. The closed-loop control law of the constraint MPC is as follows:
and 5, the unmanned ship motion control system feeds the actual value back to the controller, and returns to the step 3 for rolling optimization.
According to the method for controlling the path tracking of the unmanned surface vehicle under the complex sea area, the existing unmanned surface vehicle for ocean surveying is taken as a simulation object, simulation is carried out in MATLAB, and the effectiveness of the method is verified.
Marine unmanned ship parameter measurement: draft 0.4m, full load draft 4.1t, captain: 7.5m, 2.8m in width, 0.07 in square coefficient and 0.8m in diameter of the propeller. Two basic trajectories are designed to verify the validity of the controller, which are a straight-line path and a curved-line path, respectively, and the simulation results are shown in fig. 4 and 5 below.
As can be seen from fig. 3, the unmanned vehicle can accurately follow the reference path under time-varying disturbance, there is substantially no overshoot, and the rudder angle has a certain fluctuation to resist the disturbance. As can be seen from fig. 5, the unmanned surface vehicle can still accurately track the curved path under the time-varying disturbance of the wind current. The change of the rudder angle shows that the rudder angle changes in a small range in the whole process to resist interference, the rudder angle is smooth, and buffeting is small. The results of curve path tracking and rudder angle change show that the designed bee colony controller can effectively process the curve tracking process. The effectiveness of the marine actual ship test verification method is proved, and the unmanned ship path tracking test result shows that the unmanned ship performs the path tracking test under the actual measurement three-level sea condition, the maximum deviation of the path tracking is not more than 10m, and the method has feasibility and practicability.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but also includes other embodiments that can be derived from the technical solutions of the present invention by those skilled in the art.
Claims (7)
1. A method for controlling the path tracking of an unmanned surface vehicle under a complex sea area is characterized by comprising the following steps: the method comprises the following steps:
step 1, outputting real-time unmanned ship motion state information through the second generation Beidou satellite carried by the unmanned ship, the integrated navigation and the AIS equipment;
step 2, considering wind, wave, flow interference and steering engine response characteristics, constructing a model prediction controller to receive unmanned ship motion state information and predicting unmanned ship motion state at a future moment;
step 3, feeding back errors of the predicted values and the reference values to a rolling optimization part, and constructing an optimization function of the unmanned ship path deviation;
step 4, solving an optimal solution for the optimization function by using an improved artificial bee colony algorithm under the constraint conditions of state variables and control input to obtain an optimal command rudder angle, and outputting the optimal command rudder angle to the unmanned ship motion control system;
and 5, continuously feeding the actual value back to the controller by the unmanned ship motion control system, and returning to the step 3 for rolling optimization.
2. The method for controlling path tracking of the unmanned surface vessel under the complex sea area according to claim 1, wherein the method comprises the following steps: the unmanned ship motion state information in the step 1 comprises position information, navigational speed information and course information.
3. The calculation method of the path tracking control method for the unmanned surface vehicle under the complex sea area according to claim 1, is characterized in that: the model predictive controller constructed in the step 2 is:
wherein, in the motion coordinate of the unmanned surface vehicle, the X axis is specified to point to the true north, and the included angle between the central line of the head and the tail of the ship and the X axis, namely the ship heading angle is used
And (4) showing.
And V c Respectively flow direction and flow velocity of the flow, u r For the advancing speed of water, v r For the transverse velocity to water, for the hydration velocity
The components of the movement speed of the unmanned surface vehicle on the oxyz coordinate system along the x axis and the y axis are u and V respectively, u is the ground advance speed, V is the ground transverse moving speed, the angular speed of the bow rotating around the z axis is r, and the ground resultant speed is V = (u is the speed of the unmanned surface vehicle on the ground) 2 +v 2 ) 1/2 The drift angle β = arctan (v/u), δ is the rudder angle. Delta. For the preparation of a coating r For commanding rudder angle, K E For steering engine control gain, T E Is the steering engine time constant, m is the water surface unmanned ship mass, m x And m y For additional mass, X H 、Y H And N H For viscous fluid-like dynamics acting on the hull, X P 、Y P And N P Is propeller force, X W 、Y W And N W Is the wind power, X Wave 、Y Wave And N Wave For wave force, I ZZ Moment of inertia about vertical axis for surface unmanned craft, J ZZ To add moment of inertia, X R 、Y R And N R Is rudder force, t R Is the rudder resistance reduced by a H Is the ratio of the hull parasitic transverse force caused by steering to the rudder transverse force, x H The distance from the action center of transverse force of the steering induced hull to the gravity center of the unmanned surface vehicle F N Is a rudder positive pressure.
4. The calculation method of the path tracking control method for the unmanned surface vehicle under the complex sea area according to claim 1, is characterized in that: the concrete implementation method for predicting the unmanned ship motion state at the future moment in the step 2 is as follows:
wherein,
for the unmanned boat motion state at the future k +1 moment,
the unmanned ship motion state at the moment k is obtained. T is a unit of c Is the prediction sampling time, is the time interval between two consecutive prediction values,
and
respectively discretizing each differential equation in the model predictive controller;
wherein,
is a compensation value for the unknown term. The method is to track the path of unknown items by considering only the lateral displacement and to track the future N from the current k moment P The output of each moment is predicted:
5. the calculation method of the path tracking control method for the unmanned surface vehicle under the complex sea area according to claim 1, is characterized in that: the specific implementation method of the step 3 is as follows: based on the prediction result and the reference lateral displacement y d Calculating a path prediction error
Wherein j =1,2, \ 8230;, N P And constructing an optimization function according to the prediction error:
where Q is a weight matrix.
6. The calculation method of the path tracking control method for the unmanned surface vehicle under the complex sea area according to claim 1, is characterized in that: the improved artificial bee colony algorithm in the step 4 comprises the following steps: the method adopts a sensitivity-based free search algorithm, replaces a roulette mode with a mode of matching sensitivity and pheromone to select the honey source, and comprises the following steps:
step 4.1, calculating the fitness value f (X) of each honey source;
step 4.2, calculating pheromone nf (i) of the ith honey source:
4.3, randomly generating sensitivities S (i) -U (0, 1) of the ith follower bee;
step 4.4, finding out a bee source matched with the sensitivity of the ith following bee: randomly finding out i, satisfying nf (i) less than or equal to S (i)
Meanwhile, an improved strategy is adopted to generate a new honey source to replace the worst honey source: in each generation of circulation, the worst honey source is found, and the corresponding position is X b The new honey source position after the improved strategy is quoted as
The j-th dimension of the new location
Comprises the following steps:
7. the calculation method of the path tracking control method for the unmanned surface vehicle under the complex sea area according to claim 1, is characterized in that: the calculation method of the optimal solution in the step 4 comprises the following steps: by solving for QP quadratic form or in constraint delta min ≤δ≤δ max Equation of solution
Calculating an optimal command rudder angle:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211304752.8A CN115686002A (en) | 2022-10-24 | 2022-10-24 | Method for tracking and controlling path of unmanned surface vehicle under complex sea area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211304752.8A CN115686002A (en) | 2022-10-24 | 2022-10-24 | Method for tracking and controlling path of unmanned surface vehicle under complex sea area |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115686002A true CN115686002A (en) | 2023-02-03 |
Family
ID=85098935
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211304752.8A Pending CN115686002A (en) | 2022-10-24 | 2022-10-24 | Method for tracking and controlling path of unmanned surface vehicle under complex sea area |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115686002A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115979275A (en) * | 2023-03-20 | 2023-04-18 | 中国船舶集团有限公司第七〇七研究所 | Sea area full-coverage energy consumption optimal route planning method |
| CN117419731A (en) * | 2023-12-19 | 2024-01-19 | 青岛哈尔滨工程大学创新发展中心 | Path planning method based on multi-strategy artificial bee colony algorithm in marine environment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108897217A (en) * | 2018-07-04 | 2018-11-27 | 西北工业大学 | A kind of drive lacking waterborne vessel Trajectory Tracking Control method based on Model Predictive Control |
| CN113341953A (en) * | 2021-05-17 | 2021-09-03 | 集美大学 | Pod type unmanned ship course control method |
| CN113625725A (en) * | 2021-09-02 | 2021-11-09 | 中国舰船研究设计中心 | Unmanned surface vehicle path tracking control method |
-
2022
- 2022-10-24 CN CN202211304752.8A patent/CN115686002A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108897217A (en) * | 2018-07-04 | 2018-11-27 | 西北工业大学 | A kind of drive lacking waterborne vessel Trajectory Tracking Control method based on Model Predictive Control |
| CN113341953A (en) * | 2021-05-17 | 2021-09-03 | 集美大学 | Pod type unmanned ship course control method |
| CN113625725A (en) * | 2021-09-02 | 2021-11-09 | 中国舰船研究设计中心 | Unmanned surface vehicle path tracking control method |
Non-Patent Citations (1)
| Title |
|---|
| LIHUI DENG 等: "Research on Path Following Control of Unmanned Surface Vehicle Based on Model Predictive Control with Improved Artificial Bee Colony Algorithm", 《IEEE》, 8 July 2022 (2022-07-08), pages 1 - 6 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115979275A (en) * | 2023-03-20 | 2023-04-18 | 中国船舶集团有限公司第七〇七研究所 | Sea area full-coverage energy consumption optimal route planning method |
| CN115979275B (en) * | 2023-03-20 | 2023-05-12 | 中国船舶集团有限公司第七〇七研究所 | Energy consumption optimal route planning method for full coverage of sea area |
| CN117419731A (en) * | 2023-12-19 | 2024-01-19 | 青岛哈尔滨工程大学创新发展中心 | Path planning method based on multi-strategy artificial bee colony algorithm in marine environment |
| CN117419731B (en) * | 2023-12-19 | 2024-03-12 | 青岛哈尔滨工程大学创新发展中心 | Path planning method based on multi-strategy artificial bee colony algorithm in marine environment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Song et al. | Smoothed A* algorithm for practical unmanned surface vehicle path planning | |
| CN106773741A (en) | A kind of unmanned boat dynamic positioning system and method | |
| CN115686002A (en) | Method for tracking and controlling path of unmanned surface vehicle under complex sea area | |
| CN107942095B (en) | Wave glider actual heading prediction technique | |
| CN111026135B (en) | High-performance sailing feedforward control system and control method for unmanned ship | |
| CN112068440B (en) | Dynamic positioning control method for AUV recovery and docking based on model predictive control | |
| Gao et al. | Broad learning system-based adaptive optimal control design for dynamic positioning of marine vessels | |
| CN114879657A (en) | A Model-Free Fully Distributed Unmanned Vehicle Cooperative Time-varying Formation Control Method Based on the Satellite Coordinate System | |
| CN110262513A (en) | A kind of design method of ocean robot Trajectory Tracking Control structure | |
| Zhang et al. | Model identification of ship turning maneuver and extreme short-term trajectory prediction under the influence of sea currents | |
| Karapetyan et al. | Dynamic autonomous surface vehicle control and applications in environmental monitoring | |
| CN113625725A (en) | Unmanned surface vehicle path tracking control method | |
| Yuan et al. | Reaearch on underwater integrated navigation system based on SINS/DVL/magnetometer/depth-sensor | |
| Li et al. | Energy-efficient space–air–ground–ocean-integrated network based on intelligent autonomous underwater glider | |
| Abrougui et al. | Autopilot Design for an Unmanned Surface Vehicle Based on Backstepping Integral Technique with Experimental Results | |
| Xu et al. | Guided trajectory filtering for challenging long-range AUV navigation | |
| Rodiana et al. | Software and hardware in the loop simulation of navigation system design based on state observer using Kalman filter for autonomous underwater glider | |
| Wang et al. | Parameter Identification and Trajectory Tracking Control of Unmanned Ships Based on Measured Data | |
| Deng et al. | Research on Path Following Control of Unmanned Surface Vehicle Based on Model Predictive Control with Improved Artificial Bee Colony Algorithm | |
| Sun et al. | Predicting the Trajectory tracking control of unmanned surface vehicle based on deep learning | |
| Meng et al. | Trajectory‐tracking control of an unmanned surface vehicle based on characteristic modelling approach: Implementation and field testing | |
| CN119088044B (en) | Unmanned ship autonomous navigation control method and system based on artificial intelligence | |
| Zhou et al. | Development of an Adaptive Fuzzy Integral-Derivative Line-of-Sight Guidance Law for Bathymetric LiDAR Onboard USV | |
| Zhuang et al. | Path Planning for Unmanned Surface Vehicle based on genetic algorithm and sequential quadratic programming | |
| Shepherd et al. | Serious Sailing: Time-Optimal Control of Sailing Drones in Stochastic, Spatiotemporally Varying Wind Fields |
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 | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Deng Lihui Inventor after: Guo Tingting Inventor after: Li Yuefang Inventor after: Fan Xin Inventor after: Yuan Rubin Inventor before: Deng Lihui Inventor before: Li Yuefang Inventor before: Guo Tingting Inventor before: Fan Xin Inventor before: Yuan Rubin |