Unmanned ship trajectory tracking control method based on self-adaptive dynamic event triggering

Abstract

The invention relates to the technical field of unmanned ship trajectory tracking, in particular to an unmanned ship trajectory tracking control method based on self-adaptive dynamic event triggering, and establishes a kinematic model, an expected trajectory and a position error dynamic equation of an unmanned ship. A dynamic event triggering mechanism and self-adaptive dynamic planning are combined, a cost function containing a dynamic event triggering control law and a Hamilton function are given, and resource consumption of an unmanned ship controller is reduced. And an unknown cost function and an optimal control law are approximately obtained by using the good nonlinear approximation capability of the neural network. And applying the calculated optimal control law to a control system of the unmanned ship, and driving the unmanned ship to perform accurate tracking according to a predetermined trajectory. The method provided by the invention can effectively improve the precision of trajectory tracking of the unmanned ship, and saves system calculation and communication resources.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned boat trajectory tracking, and in particular to an unmanned boat trajectory tracking control method based on adaptive dynamic event triggering. Background Art

[0002] The flexibility and adaptability of unmanned boats enable them to demonstrate excellent performance in complex and changing marine environments. In the field of unmanned boat trajectory tracking, advanced navigation and control technologies are used to accurately track preset navigation trajectories, complete complex tasks autonomously, reduce dependence on personnel, and improve operational safety and efficiency.

[0003] Traditional methods for tracking the trajectory of unmanned boats in complex sea areas mainly include PID control, sliding mode control, and model predictive control. These methods have their own characteristics, but they also have shortcomings. PID control is simple and easy, but it has limited effect when dealing with nonlinear and time-varying characteristics in complex sea areas. Sliding mode control has strong robustness, but it is prone to jitter, which affects control accuracy. Model predictive control can handle uncertainty, but it has high computational complexity and high hardware requirements. Summary of the invention

[0004] In view of this, the purpose of the present invention is to propose an unmanned boat trajectory tracking control method based on adaptive dynamic event triggering to solve the problems of high computational complexity, high energy consumption and high communication burden of the existing methods.

[0005] Based on the above purpose, the present invention provides an unmanned boat trajectory tracking control method based on adaptive dynamic event triggering, comprising the following steps:

[0006] S1. Establish the kinematic model of the unmanned boat, define the expected trajectory and tracking error, and establish the dynamic equation of the position error of the unmanned boat;

[0007] S2. Combining the dynamic event triggering mechanism and adaptive dynamic programming, the cost function and Hamilton function containing the dynamic event triggering control law are obtained;

[0008] S3, using the good nonlinear approximation ability of neural networks to approximately obtain unknown and continuous cost functions and optimal dynamic control laws;

[0009] S4. Using the calculated optimal control law, the unmanned boat is driven to accurately track the predetermined trajectory.

[0010] Preferably, in step S1, the kinematic model of the unmanned boat is established as follows:

[0011]

[0012] Among them, w, v and are the longitudinal velocity, lateral velocity and bow angular velocity of the unmanned boat, respectively, and x, y and are the lateral position, longitudinal position and bow offset angle of the unmanned boat, and are the first-order differentials of the lateral position, longitudinal position and bow offset angle of the unmanned boat respectively.

[0013] Preferably, in step S1, the expected trajectory is defined as p d =[x d (t),y d (t)] T , where x d (t), y d (t) are the horizontal and vertical coordinates of the expected position, respectively. The superscript T indicates transposition.

[0014] Preferably, in step S1, a dynamic equation of the position error of the unmanned boat is established according to the error between the current position of the unmanned boat and the expected trajectory. The dynamic equation of the position error is: in, is the desired heading angle, is the angle between the unmanned boat’s forward direction and the trajectory tracking error vector, are the desired position abscissa, ordinate and first-order differential of the position error respectively.

[0015] Preferably, in step S2, the cost function is expressed as J(u,ρ e ,t), the cost function contains two integral process terms, which respectively reflect the total measure of dynamic tracking error and the total measure of energy consumption in the control process. In the cost function, ρ e is the tracking error, u is the control input variable, and t is time.

[0016] Preferably, the Hamilton function is used to describe the dynamic characteristics and energy state of the unmanned boat system. The Hamilton function containing the dynamic event triggering control law μ is expressed as in, Denotes the cost function J(μ,ρ e ,t) find the gradient.

[0017] Preferably, the dynamic event triggering mechanism sets a dynamic event triggering condition t k+1 = inf{t∈R||e u (t)|≥k0χ(t)+k1|u(t)|+k2}, where t k is the event triggering time, χ(t) is the dynamic event triggering factor, e u(t) is the event trigger error threshold, k0, k1, and k2 are positive constants. When the state change of the unmanned boat exceeds the preset threshold, the state update and control signal transmission are triggered, otherwise the current state and control input are kept unchanged.

[0018] Preferably, the neural network is defined as a value network J i (ρ e ) and the policy network μ i , the approximation error caused by the approximate characteristics of the neural network is ε(ρ e ), iteratively update the network weights of the neural network according to the function value of the error function Until it is updated to the best.

[0019] Preferably, when the network weights are iteratively updated to the optimum, the optimal dynamic control law μ is calculated. * , the calculated optimal control law is applied to the control system of the unmanned boat, driving the unmanned boat to accurately track the predetermined trajectory.

[0020] Beneficial effects of the present invention:

[0021] (1) High accuracy of trajectory tracking: The method proposed in the present invention can combine the flexibility of adaptive control and the efficiency of event-triggered control, and can cope with various uncertain factors in complex sea areas by adjusting the control strategy in real time, thereby improving the accuracy of unmanned boat trajectory tracking.

[0022] (2) Saving communication and computing resources: The method proposed in the present invention can dynamically adjust the trigger conditions according to the changes in the system status and transmit control signals only when necessary, thereby significantly reducing communication and computing resources and lowering the overall energy consumption of the system.

[0023] (3) Strong adaptability: The method proposed in the present invention can learn and adapt to the dynamic characteristics of the system in real time by introducing adaptive dynamic programming, thereby enhancing the adaptability of the system.

[0024] (4) Good scalability: The control method proposed in this invention can adapt to different unmanned boat platforms and complex sea environments. At the same time, with the continuous development of technology, this method can also be combined with other advanced control methods to further improve the trajectory tracking control performance of the unmanned boat. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A schematic diagram of the trajectory tracking of an unmanned boat according to an embodiment of the present invention;

[0027] Figure 2 The present invention is a flow chart of the unmanned boat trajectory tracking control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0029] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0030] The embodiment of this specification provides a method for tracking and controlling an unmanned boat trajectory based on adaptive dynamic event triggering, comprising the following steps:

[0031] S1. Aiming at the problem of unmanned boat trajectory tracking, the present invention establishes a kinematic model of the unmanned boat, defines the expected trajectory and tracking error, and provides a dynamic equation for the position error of the unmanned boat.

[0032] In order to simulate the motion of the unmanned boat, the kinematic model of the unmanned boat is established as follows:

[0033]

[0034] Among them, w, v and r are the longitudinal velocity, lateral velocity and bow angular velocity of the unmanned boat respectively, and x, y and are the lateral position, longitudinal position and bow offset angle of the unmanned boat, and are the first-order differentials of the lateral position, longitudinal position and bow offset angle of the unmanned boat respectively.

[0035] like Figure 1 As shown, the expected trajectory is defined as p d =[x d (t),yd (t)] T , where x d (t), y d (t) are the horizontal coordinate and vertical coordinate of the expected position respectively. According to the current position of the unmanned boat and the expected trajectory, the trajectory tracking error of the unmanned boat in the inertial coordinate is:

[0036]

[0037] Among them, x e (t), y e (t) are the position abscissa error and position ordinate error respectively. The position error of the unmanned boat is defined as In order to calculate the deviation between the unmanned boat and the desired trajectory in real time, the control system can flexibly adjust the strategy, reduce the tracking error, cope with complex environments, ensure navigation safety, and optimize the control algorithm. According to the error between the current position of the unmanned boat and the desired trajectory, the dynamic equation of the position error is given as

[0038]

[0039] in, is the desired heading angle, is the angle between the unmanned boat’s forward direction and the trajectory tracking error vector, are the desired position abscissa, ordinate and first-order differential of position error, respectively. About The vector of It's about x e ,y e , function.

[0040] S2. By designing a dynamic event-triggered controller, the communication and computing resource consumption of the unmanned boat controller is saved, and the cost function and Hamilton function containing the dynamic event-triggered control law are further given.

[0041] In order to optimize the dynamic error and energy consumption of the system during the control process, as well as the terminal tracking error of the system under control constraints, a cost function including tracking error and control input variables is designed as follows:

[0042]

[0043] Among them, Q and R are semi-positive coefficient matrices, and F is a positive definite coefficient matrix. is the dynamic control law, u T (t) is the transpose of u(t), t f is the terminal moment, the first integral process term It reflects the total measure of the dynamic tracking error in the control process. The second integral process term Reflects the overall measure of control energy consumption.

[0044] The dynamic event trigger mechanism can adjust the control strategy in real time according to the current position error of the unmanned boat, changes in the external environment and other factors, avoiding frequent controller signal updates, thereby significantly reducing computing and communication resources, actuator losses and energy consumption. Set a dynamic event trigger condition based on the change in the state of the unmanned boat or the error threshold. When the state change of the unmanned boat exceeds the preset threshold, the state update and control signal transmission are triggered, otherwise the current state and control input remain unchanged.

[0045] Set the event trigger time as t k , the corresponding event-triggered control law is u(t)=μ(t k ), The trigger condition is designed as: t k+1 = inf{t∈R||e u (t)|≥k0χ(t)+k1|u(t)|+k2}, where χ(y) is the dynamic event trigger factor, expressed as is the initial value of the dynamic event trigger factor, is a positive constant, the event trigger error e u (t) is expressed as e u (t) = u(t) - μ(t k ), k0, k1, k2 are positive constants.

[0046] The cost function of the control law μ containing dynamic event triggering is defined as

[0047]

[0048] The Hamilton function describes the dynamic characteristics and energy state of the unmanned boat system. By constructing the Hamilton system model, the stability and performance of the unmanned boat in the trajectory tracking process can be analyzed. The Hamilton function containing the dynamic event triggering control law μ is expressed as

[0049]

[0050] in, Denotes the cost function J(μ,ρ e ,t) find the gradient.

[0051] make Get the optimal dynamic control law

[0052] Since the cost function J(ρe ) and the optimal dynamic control law μ * It is very difficult to solve the unknown and continuous function accurately.

[0053] S3. Use the good nonlinear approximation ability of neural networks to approximate unknown and continuous cost functions and optimal dynamic control laws. The deep architecture and flexible learning mechanism of neural networks enable them to accurately capture the nonlinear characteristics of system behavior, thereby achieving accurate simulation of cost functions and effective approximation of optimal dynamic control laws. This not only improves the intelligence level of the control system, but also significantly enhances the system's ability to adapt to complex environments and changing conditions. Design a neural network for

[0054]

[0055] in, is the neural network weight vector, is the transpose of the network weight vector, φ(ρ e ) is the activation function.

[0056] The approximation error ε(ρ caused by the approximation characteristics of the neural network e )for

[0057]

[0058] According to the neural network approximation error ε(ρ e ), and further define the error function as Iteratively update the network weights of the neural network according to the function value of the error function:

[0059]

[0060] in, is the current network weight, is the network weight updated in the last iteration, is the partial derivative of the error function with respect to the last updated network weight, and δ>0 is the learning rate of the neural network.

[0061] Determine the current network weight The network weights updated with the previous iteration Whether the error value exceeds the preset error threshold ε * ,when When , the iteration stops, and the current network weight is the expected weight, when , continue to execute the neural network weight update step.

[0062] S4. When the network weights are iteratively updated to the optimal value, that is, Calculate the optimal dynamic control law

[0063]

[0064] The calculated optimal control law is applied to the control system of the unmanned boat to drive the unmanned boat to accurately track the predetermined trajectory. At the same time, according to the dynamic event trigger mechanism, the control strategy and transmission frequency are adjusted in real time to achieve efficient and accurate trajectory tracking.

[0065] Through the above specific implementation methods, the control method proposed in the present invention can adapt to different unmanned boat platforms and complex sea environments.

[0066] Those skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present invention is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity. Any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A trajectory tracking control method for an unmanned boat based on adaptive dynamic event triggering, characterized in that: The method comprises the following steps: S1. Establish the kinematic model of the unmanned boat, define the expected trajectory and tracking error, and establish the dynamic equation of the position error of the unmanned boat; S2. Combining the dynamic event triggering mechanism and adaptive dynamic programming, the cost function and Hamilton function containing the dynamic event triggering control law are obtained; S3, using the good nonlinear approximation ability of neural networks to approximately obtain unknown and continuous cost functions and optimal dynamic control laws; S4. Using the calculated optimal control law, the unmanned boat is driven to accurately track the predetermined trajectory.

2. The unmanned boat trajectory tracking control method based on adaptive dynamic event triggering according to claim 1 is characterized in that: In step S1, the kinematic model of the unmanned boat is established as: Among them, w, v and r are the longitudinal velocity, lateral velocity and bow angular velocity of the unmanned boat respectively, and x, y and are the lateral position, longitudinal position and bow offset angle of the unmanned boat, and are the first-order differentials of the lateral position, longitudinal position and bow offset angle of the unmanned boat respectively.

3. The unmanned boat trajectory tracking control method based on adaptive dynamic event triggering according to claim 1 is characterized in that: In step S1, the expected trajectory is defined as p d =[x d (t),y d (t)] Τ , where x d (t), y d (t) are the horizontal and vertical coordinates of the expected position, respectively. The superscript T indicates transposition.

4. The unmanned boat trajectory tracking control method based on adaptive dynamic event triggering according to claim 3 is characterized in that: In step S1, the position error dynamic equation of the unmanned boat is established according to the error between the current position of the unmanned boat and the expected trajectory. The position error dynamic equation is: in, is the desired heading angle, is the angle between the unmanned boat’s forward direction and the trajectory tracking error vector, are the desired position abscissa, ordinate and first-order differential of the position error respectively.

5. The unmanned boat trajectory tracking control method based on adaptive dynamic event triggering according to claim 1 is characterized in that: In step S2, the cost function is expressed as J(u,ρ e ,t), the cost function contains two integral process terms, which respectively reflect the total measure of dynamic tracking error and the total measure of energy consumption in the control process. In the cost function, ρ e is the tracking error, u is the control input variable, and t is time.

6. The unmanned boat trajectory tracking control method based on adaptive dynamic event triggering according to claim 5 is characterized in that: The Hamilton function is used to describe the dynamic characteristics and energy state of the unmanned boat system. The Hamilton function containing the dynamic event triggering control law μ is expressed as in, Denotes the cost function J(μ,ρ e ,t) find the gradient.

7. The unmanned boat trajectory tracking control method based on adaptive dynamic event triggering according to claim 1 is characterized in that: The dynamic event triggering mechanism sets a dynamic event triggering condition t k+1 = inf{t∈R||e u (t)|≥k0χ(t)+k1|u(t)|+k2}, where t k is the event triggering time, χ(t) is the dynamic event triggering factor, e u (t) is the event trigger error threshold, k0, k1, and k2 are positive constants. When the state change of the unmanned boat exceeds the preset threshold, the state update and control signal transmission are triggered, otherwise the current state and control input remain unchanged.

8. The unmanned boat trajectory tracking control method based on adaptive dynamic event triggering according to claim 1 is characterized in that: The neural network is defined as the value network J i (ρ e ) and the policy network μ i , the approximation error caused by the approximate characteristics of the neural network is ε(ρ e ), iteratively update the network weights of the neural network according to the function value of the error function Until it is updated to the best.

9. The unmanned boat trajectory tracking control method based on adaptive dynamic event triggering according to claim 1 is characterized in that: When the network weights are iteratively updated to the optimum, the optimal dynamic control law μ is calculated. * , the calculated optimal control law is applied to the control system of the unmanned boat, driving the unmanned boat to accurately track the predetermined trajectory.

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