Unmanned ship adaptive trajectory tracking control method based on event driving

Abstract

The invention relates to the technical field of artificial intelligence, and relates to an event-driven unmanned ship self-adaptive trajectory tracking control method, which comprises the following steps of: performing quantization operation on a control input signal of an unmanned ship through a uniform quantizer, establishing a linear model to analyze and quantize dynamic characteristics, designing a control law and a self-adaptive rate of the unmanned ship, and performing trajectory tracking control on the unmanned ship. Wherein the control law processes uncertainty and external disturbance of the unmanned ship system through sliding mode control, and the adaptive rate is used for estimating unknown parameters; then a time-varying threshold event triggering mechanism is utilized, the event triggering mechanism and sliding mode control are combined, a quantitative feedback control system is constructed, finally, the stability of the quantitative feedback control system is proved based on the Lyapuno theory, and an efficient control effect can be achieved without knowing quantitative parameters in advance in combination with event triggering and input quantization. And different triggering effects are obtained aiming at different states by utilizing a nonlinear triggering mechanism, so that network resources can be reasonably utilized, and the stability of control is effectively ensured.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and specifically to an event-driven adaptive trajectory tracking control method for unmanned vessels. Background Technology

[0002] In the field of marine engineering, unmanned surface vehicles (USVs) play an indispensable role. Due to their high degree of autonomy in the underwater environment, USVs are widely used in various fields such as marine surveying, emergency rescue, and military applications. From these application perspectives, trajectory tracking control is a key fundamental control challenge for USVs and plays an extremely important role. Trajectory tracking refers to the process by which the USV, starting from any initial position, navigates into the desired course under the guidance of the control system and tracks the required trajectory with the smallest possible error. During actual navigation, ships are easily affected by various external factors such as wind, waves, and currents, which poses numerous challenges to trajectory tracking control. In recent years, scholars have conducted extensive research on the trajectory tracking problem of unmanned surface vessels (USVs) and have made some progress, such as sliding mode control, fuzzy control, neural network control, and robust control. However, most literature focuses on designing better control schemes to improve the accuracy and consistency of USV trajectory tracking. Unlike traditional surface ships, USVs rely entirely on wireless channels for communication, thus requiring consideration of communication limitations and actuator execution frequencies in maritime practice. Control signals are transmitted through communication channels, but the bandwidth of maritime communication channels is limited. To conserve communication resources, it is necessary to consider input quantization when studying USV motion control strategies.

[0003] While input quantization is more in line with the basic operation of controllers in maritime practice, it does not fundamentally save energy. Therefore, event-triggered mechanisms have received much attention in recent years. For example, CN116224798A discloses an event-triggered trajectory tracking control method for autonomous underwater vehicles (AUVs), including: constructing a kinematic and dynamic model of the AUV based on the system structure characteristics of a three-degree-of-freedom AUV; designing a motion controller for the AUV based on the backstepping method and obtaining virtual control inputs to derive the tracking error equation; designing a dynamic controller for stabilizing the tracking error using a global sliding mode control method; and introducing an event-triggered mechanism to track the trajectory of the AUV based on a fixed threshold triggering control strategy. CN119045481A discloses a method with... An event-triggered mechanism and signal quantization-based adaptive trajectory tracking control method for unmanned surface vessels (USVs) includes: quantizing the control signal using a uniform quantizer and describing the input quantization process and external disturbances using a linear analysis model; estimating the quantized state feedback information, system uncertainties, and external disturbances using a neural network observer; designing a quantized feedback controller using the observation results of the neural network observer by combining backstepping, dynamic surface techniques, and an event-triggered mechanism; and proving the observation error of the neural network observer and the stability of the designed USV adaptive trajectory tracking control system with event-triggered mechanism and signal quantization based on Lyapunov stability theory. However, existing event-triggered mechanisms are designed for specific conditions and cannot be differentiated according to different states. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and to design an event-driven adaptive trajectory tracking control method for unmanned vessels. By combining event triggering and input quantization, efficient control can be achieved without prior knowledge of quantization parameters. Moreover, by utilizing a nonlinear triggering mechanism, different triggering effects can be obtained for different states, which can make reasonable use of network resources and effectively ensure the stability of control.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An event-driven adaptive trajectory tracking control method for unmanned surface vessels includes: S1. The control input signal of the unmanned vessel is quantized by a uniform quantizer, and a linear model is established to analyze the dynamic characteristics of the quantization. The control signal is discretized by the quantization operation to adapt to the communication bandwidth limitation. S2. Design the control law and adaptive rate of the unmanned ship. The control law uses sliding mode control to handle the uncertainty and external disturbance of the unmanned ship system, and the adaptive rate is used to estimate unknown parameters. S3. By utilizing a time-varying threshold event triggering mechanism, the event triggering mechanism is combined with sliding mode control to construct a quantitative feedback control system. The time-varying threshold event triggering mechanism dynamically adjusts the update frequency of the unmanned vessel trajectory based on the state error. S4. Prove the stability of the quantized feedback control system based on Lyapuno theory.

[0006] Further, step S1 specifically includes: S11. Construct the kinematic and dynamic models of the USV as follows: , In the formula, Let be the transformation matrix. The inertia matrix, For the Coriolis and centripetal force matrices, Here is the damping matrix; , The location of the unmanned vessel. For the bow angle of the unmanned vessel; , These are the forward speed, lateral speed, and bow angular velocity of the unmanned vessel. To control the quantization value of the input, , , , , , , , , , , , For the quality of USV, For added mass, For the center of gravity of the USV, Let be the moment of inertia about the vertical axis. These are fluid dynamic coefficients; S12, Control input Quantization is performed using a uniform quantizer; the result is... ,in To quantify the level; S13, Assuming external interference , , , , The upper limit of unknown external time-varying disturbance variables, all of which are greater than 0; assume an ideal reference trajectory. It is continuously differentiable, and and its differential term Both are bounded; S14, Order ,definition: , In the formula , It is time-varying and unknown. , As its symbol is obtained as a constant throughout the quantization process, due to , Bounded; hour, Bounded, therefore .

[0007] Further, step S2 specifically includes: S21. Define the uncertainty term Then the kinematic and dynamic model of the USV simplifies to: , S22. Define the trajectory tracking error and its derivative. Taking the derivative of the trajectory tracking error again, we get: = ; S23, Define the sliding surface ,in ;but Sliding surface As a core indicator for error monitoring in trajectory control, when When the value is close to 0, it means that the USV is not only close to the desired trajectory at its current position, but also that the rate of change of the deviation is converging, which can ensure the stability of trajectory tracking from a dynamic trend perspective. S24. Combining step S14, we obtain: ; S25. According to Young's inequality: Assuming , ,because It is a rotation matrix. It is a symmetric positive definite matrix, therefore , , Therefore, we can conclude that: ; S26, combined with S24 and S15, shows that ,so Combining S24 and S25, we get ,in It is a normal number, let ,but ; S27. Using time-varying parameters express The lower limit and intermediate control signal are designed as follows: Adaptability rate ,in All parameters are positive; parameter estimation error ,in For parameters The adaptive estimate; The true value is obtained by adjusting... To get closer to reality μ This enhances the adaptability to quantizer uncertainties and avoids [the problem caused by] [the uncertainty of the quantizer]. The unknown leads to a decrease in control precision, due to ,so .

[0008] Furthermore, step S3 specifically includes: S31. A time-varying threshold event triggering control strategy based on quantization, as follows: , ,in , ; This is the proportional coefficient for the time-varying trigger threshold, and its value is a positive real number ( By dynamically adjusting the trigger threshold, communication resources and control precision are balanced; S32, triggered by the condition It can be seen that there exists a continuously time-varying coefficient. satisfy and Make , because ,make , ,get , because , Established, , These are auxiliary variables introduced during the derivation process, used to include intermediate control signals. Quantized control input The nonlinear inequality is transformed into a standard quadratic form, which facilitates the analysis of the constraint relationship between the two. as auxiliary variables The lower limit constant, Therefore, we get , ;make ,but , ,definition ,in .

[0009] Furthermore, step S4 specifically includes: S41. Define Lyapunov functions: The Lyapunov function V represents the sliding surface error. and parameter estimation error Integration, sliding surface It incorporates the deviation between the actual and expected USV trajectories. and the rate of change of deviation , This represents the energy of the sliding surface. The smaller the value, the better the dynamic convergence of the USV trajectory deviation, which ensures the stability of trajectory tracking from a trend perspective and avoids the situation where the current position is aligned but deviates again due to improper speed. The energy reflecting the estimation error of the disturbance parameters is due to the characteristics of disturbances such as wind, waves, and ocean currents (using...). Characterization is difficult to measure precisely, so estimations are used. and the true value error The accuracy of interference compensation is monitored; the smaller V is, the more stable the USV trajectory tracking is, the more accurate the interference compensation is, and the smaller the current deviation is, which lays the foundation for subsequent proof that the trajectory can converge stably. S42. Prove the derivative of the Lyapunov function V. Negative definite, for Taking the derivative, we get: By proving the derivative Negative constant indicates that regardless of surface environmental disturbances or signal discontinuities caused by quantization and event triggering, the USV's trajectory tracking error... Ultimately, it will converge to a small neighborhood, strictly ensuring that the designed control strategy can enable the USV to stably track the desired trajectory, so that the entire trajectory control scheme has both engineering feasibility and theoretical stability support. It is the rate of change of energy on the sliding surface, as determined by the sliding mode control law. ,let and Reverse (i.e.) This ensures that the energy of the sliding surface continues to decrease, corresponding to the dynamic convergence of the USV trajectory deviation; It is the rate of change of the disturbance compensation energy, through the adaptive law ,let The energy continues to decrease ( <0, interference compensation becomes increasingly accurate; S43, Substitution ,have to: By substitution In subsequent derivation Ensure in negative constant This allows the sliding surface energy to continuously decrease, thereby achieving dynamic convergence of the USV trajectory deviation. S44, Order , Then the inequality Established, among which ; S45, due to ,but , The simplifying variables introduced to derive Lyapunov stability. ,then Combining with step S43, we get: ; S46, Substitute adaptive rate have to By substituting the adaptive law In the derivation Negative guarantee The energy continues to decrease ( <0, making interference compensation increasingly accurate. because ,but Thus obtain , Combining step S45, we get: , consider The inequalities obtained from step S44 are as follows: , Thus obtain And thus obtain ,in , , , This illustrates that when external interference occurs... Since the representation is bounded, the energy of the Lyapunov function will continue to decrease and converge to a small neighborhood. The trajectory tracking error and disturbance compensation error of the USV will stabilize within an acceptable range. It is mathematically proven that the designed quantitative feedback control system can enable the unmanned surface vessel to stably track the desired trajectory. Solve the inequalities get: ,then ,Pick Much larger hour, , , , , , The obtained error will converge to a small residual, eventually remaining consistent and bounded. The convergence of the sliding surface indicates that the dynamic rate of change of the USV trajectory deviation and the current deviation are both approaching 0; When the trajectory error converges to 0, it means that the actual position and attitude of the USV will approach the desired trajectory infinitely. That is, the accuracy of interference compensation is approaching perfection, meaning that the estimation of the interference characteristics of wind, waves, and ocean currents is becoming increasingly accurate; S47, Yes , and Differentiating both sides yields ,therefore ;error The growth rate was The rate of change is limited, regardless of the disturbance from the waves, the ideal control command The rate of change has an upper limit. ),error It won't suddenly surge; S48. Obtained from step S27 It is continuous, and yes and The function, and It is bounded, therefore there exists a satisfying normal numbers ,because From step S47, we obtain ,thereby , That is, there exists a positive constant. satisfy ,so To prevent an unlimited number of triggers, the instruction update interval of the event-triggered mechanism has a lower limit, so as not to issue instructions frequently, while ensuring that the error is controllable and does not affect the trajectory, so that the USV can still stably track the desired route with fewer instructions.

[0010] This invention constructs a quantized feedback control system based on an event-triggered sliding mode controller. Combining input quantization and adaptive laws, it achieves a closed-loop control system for unmanned surface vessel (USV) trajectory tracking. Sliding mode control handles system uncertainties and external disturbances; the event-triggered mechanism dynamically adjusts the control update frequency based on state errors; input quantization discretizes the control signal to adapt to communication bandwidth limitations; and an adaptive law is designed to estimate unknown parameters (such as upper limits of disturbances) to enhance robustness. Ultimately, a closed-loop system of "quantized input → triggered control → sliding mode adjustment → trajectory tracking" is formed, achieving a balance between communication resource conservation and tracking stability. The sliding mode control is manifested in the definition of the sliding surface. The system state is driven to slide towards the sliding surface by designing control laws, and the event triggering mechanism is reflected in the control signals. It is not continuously updated, but only when the triggering condition is met; the point of convergence is that the output of the sliding mode control law is "sampled" by the event triggering mechanism before execution; all variables in the control law (such as the sliding mode variable s) are calculated based on the "state at the time of the most recent trigger", rather than the real-time state; this preserves the robustness of sliding mode control and reduces the communication frequency through event triggering.

[0011] Compared with the prior art, the present invention has the following advantages: (1) Unlike the traditional USV trajectory tracking method that relies on continuous control signals, this invention proposes a novel event triggering mechanism. By dynamically adjusting the triggering threshold, it can significantly reduce the use of communication network bandwidth while maintaining system stability. This is especially suitable for maritime applications with limited bandwidth, thus enhancing the practicality of the strategy.

[0012] (2) Unlike existing controller designs, this invention proposes a linear analysis model for the input quantization process. This model does not require prior knowledge of quantization during controller design, thus improving its practical applicability and enabling its widespread application in fields such as artificial intelligence. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of the method of the present invention.

[0015] Figure 2 This is a diagram showing the ship trajectory tracking results provided in Embodiment 2 of the present invention.

[0016] Figure 3The image shows the tracking results of the ship's position and bow roll angle provided in Embodiment 2 of the present invention.

[0017] Figure 4 The ship position and bow roll angle tracking error diagram provided in Embodiment 2 of the present invention Figure 5 This is a comparison diagram of control inputs before and after quantization provided in Embodiment 2 of the present invention.

[0018] Figure 6 This is a time interval diagram between two consecutive event-triggered samples provided in Embodiment 2 of the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Example 1: like Figure 1 As shown, this embodiment provides an event-driven adaptive trajectory tracking control method for unmanned surface vessels, including: S1. The control input signal of the unmanned vessel is quantized using a uniform quantizer, and a linear model is established to analyze the dynamic characteristics of the quantization. The control signal is discretized through quantization to adapt to communication bandwidth limitations. The specific process is as follows: S11. Construct the kinematic and dynamic models of the USV as follows: , In the formula, Let be the transformation matrix. The inertia matrix, For the Coriolis and centripetal force matrices, Here is the damping matrix; , The location of the unmanned vessel. For the bow angle of the unmanned vessel; , These are the forward speed, lateral speed, and bow angular velocity of the unmanned vessel. To control the quantization value of the input, , , , , , , , , , , , For the quality of USV, For added mass, For the center of gravity of the USV, Let be the moment of inertia about the vertical axis. These are fluid dynamic coefficients; S12, Control input Quantization is performed using a uniform quantizer; the result is... ,in To quantify the level; S13, Assuming external interference , , , , The upper limit of unknown external time-varying disturbance variables, all of which are greater than 0; assume an ideal reference trajectory. It is continuously differentiable, and and its differential term Both are bounded; S14, Order ,definition: , In the formula , It is time-varying and unknown. , As its symbol is obtained as a constant throughout the quantization process, due to , Bounded; hour, Bounded, therefore .

[0021] S2. Design the control law and adaptive rate of the unmanned surface vessel (USV). The control law uses sliding mode control to handle uncertainties and external disturbances in the USV system, and the adaptive rate is used to estimate unknown parameters. The specific process is as follows: S21. Define the uncertainty term Then the kinematic and dynamic model of the USV simplifies to: , S22. Define the trajectory tracking error and its derivative. Taking the derivative of the trajectory tracking error again, we get: = ; S23, Define the sliding surface ,in ;but Sliding surface As a core indicator for error monitoring in trajectory control, when When the value is close to 0, it means that the USV is not only close to the desired trajectory at its current position, but also that the rate of change of the deviation is converging, which can ensure the stability of trajectory tracking from a dynamic trend perspective. S24. Combining step S14, we obtain: ; S25. According to Young's inequality: Assuming , ,because It is a rotation matrix. It is a symmetric positive definite matrix, therefore , , Therefore, we can conclude that: ; S26, combined with S24 and S15, shows that ,so Combining S24 and S25, we get ,in It is a normal number, let ,but ; S27. Using time-varying parameters express The lower limit and intermediate control signal are designed as follows: Adaptability rate ,in All parameters are positive; parameter estimation error ,in For parameters The adaptive estimate; The true value is obtained by adjusting... To get closer to reality μ This enhances the adaptability to quantizer uncertainties and avoids [the problem caused by] [the uncertainty of the quantizer]. The unknown leads to a decrease in control precision, due to ,so .

[0022] S3. Utilizing a time-varying threshold event triggering mechanism, this mechanism is combined with sliding mode control to construct a quantized feedback control system. The time-varying threshold event triggering mechanism dynamically adjusts the update frequency of the unmanned surface vessel's trajectory based on the state error; specifically including: S31. A time-varying threshold event triggering control strategy based on quantization, as follows: , ,in , ; This is the proportional coefficient for the time-varying trigger threshold, and its value is a positive real number ( By dynamically adjusting the trigger threshold, communication resources and control precision are balanced. S32, triggered by the condition It can be seen that there exists a continuously time-varying coefficient. satisfy and Make , because ,make , ,get , because , Established, , These are auxiliary variables introduced during the derivation process, used to include intermediate control signals. Quantized control input The nonlinear inequality is transformed into a standard quadratic form, which facilitates the analysis of the constraint relationship between the two. as auxiliary variables The lower limit constant, Therefore, we get , ;make ,but , ,definition ,in .

[0023] S4. Prove the stability of the quantized feedback control system based on Lyapuno theory, specifically as follows: S41. Define Lyapunov functions: The Lyapunov function V represents the sliding surface error. and parameter estimation error Integration, sliding surface It incorporates the deviation between the actual and expected USV trajectories. and the rate of change of deviation , This represents the energy of the sliding surface. The smaller the value, the better the dynamic convergence of the USV trajectory deviation, which ensures the stability of trajectory tracking from a trend perspective and avoids the situation where the current position is aligned but deviates again due to improper speed. The energy reflecting the estimation error of the disturbance parameters is due to the characteristics of disturbances such as wind, waves, and ocean currents (using...). Characterization is difficult to measure precisely, so estimations are used. and the true value error The accuracy of interference compensation is monitored; the smaller V is, the more stable the USV trajectory tracking is, the more accurate the interference compensation is, and the smaller the current deviation is, which lays the foundation for subsequent proof that the trajectory can converge stably. S42. Prove the derivative of the Lyapunov function V. Negative definite, for Taking the derivative, we get: By proving the derivative Negative constant indicates that regardless of surface environmental disturbances or signal discontinuities caused by quantization and event triggering, the USV's trajectory tracking error... Ultimately, it will converge to a small neighborhood, strictly ensuring that the designed control strategy can enable the USV to stably track the desired trajectory, so that the entire trajectory control scheme has both engineering feasibility and theoretical stability support. It is the rate of change of energy on the sliding surface, as determined by the sliding mode control law. ,let and Reverse (i.e.) This ensures that the energy of the sliding surface continues to decrease, corresponding to the dynamic convergence of the USV trajectory deviation; It is the rate of change of the disturbance compensation energy, through the adaptive law ,let The energy continues to decrease ( <0, interference compensation becomes increasingly accurate; S43, Substitution ,have to: By substitution In subsequent derivation Ensure in negative constant This allows the sliding surface energy to continuously decrease, thereby achieving dynamic convergence of the USV trajectory deviation. S44, Order , Then the inequality Established, among which ; S45, due to ,but , The simplifying variables introduced to derive Lyapunov stability. ,then Combining with step S43, we get: ; S46, Substitute adaptive rate have to By substituting the adaptive law In the derivation Negative guarantee The energy continues to decrease ( <0, making the compensation for interference increasingly accurate; because ,but Thus obtain , Combining step S45, we get: , consider The inequalities obtained from step S44 are as follows: , Thus obtain And thus obtain ,in , , This illustrates that when external interference occurs... Since the representation is bounded, the energy of the Lyapunov function will continue to decrease and converge to a small neighborhood. The trajectory tracking error and disturbance compensation error of the USV will stabilize within an acceptable range. It is mathematically proven that the designed quantitative feedback control system can enable the unmanned surface vessel to stably track the desired trajectory. Solve the inequalities get: ,then ,Pick Much larger hour, , , , , , The obtained error will converge to a small residual, eventually remaining consistent and bounded. The convergence of the sliding surface indicates that the dynamic rate of change of the USV trajectory deviation and the current deviation are both approaching 0; When the trajectory error converges to 0, it means that the actual position and attitude of the USV will approach the desired trajectory infinitely. That is, the accuracy of interference compensation is approaching perfection, meaning that the estimation of the interference characteristics of wind, waves, and ocean currents is becoming increasingly accurate; S47, Yes , and Differentiating both sides yields ,therefore ;error The growth rate was The rate of change is limited, regardless of the disturbance from the waves, the ideal control command The rate of change has an upper limit. ),error It won't suddenly surge; S48. Obtained from step S27 It is continuous, and yes and The function, and It is bounded, therefore there exists a satisfying normal numbers ,because From step S47, we obtain ,thereby , That is, there exists a positive constant. satisfy ,so To prevent an unlimited number of triggers, the instruction update interval of the event-triggered mechanism has a lower limit, so as not to issue instructions frequently, while ensuring that the error is controllable and does not affect the trajectory, so that the USV can still stably track the desired route with fewer instructions.

[0024] Example 2: To verify the effectiveness of the method described in Example 1, this example uses MATLAB to conduct a trajectory tracking control simulation experiment. Figure 2-5 The initial state of the controlled object is shown below. , and The expected trajectory is , and The results include trajectory tracking results, ship position and heading angle tracking results, tracking error and control input comparison before and after quantization, and time interval diagram between sampling triggered by two consecutive events. The results are as follows: Figure 2-6 As shown, Figure 2 and Figure 3 The image shows the ship trajectory tracking results and error graph. Figure 4 The simulation results show that the designed controller performs well in controlling the ship's position and bow angle, effectively tracking the ship's trajectory. (Comparison) Figure 5 and Figure 6 It is evident that the quantized control input signal, while maintaining system performance, significantly reduces the frequency and amplitude of execution changes, thereby alleviating the burden on network communication and improving engineering practicality.

[0025] The simulation results of this embodiment confirm that the method described in Embodiment 1, which considers the quantization of control inputs and the event triggering mechanism in the control system, can achieve better ship trajectory tracking while ensuring the stability of the control system.

[0026] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An event-driven adaptive trajectory tracking control method for unmanned surface vessels, characterized in that, include: S1. The control input signal of the unmanned vessel is quantized by a uniform quantizer, and a linear model is established to analyze the dynamic characteristics of the quantization. The control signal is discretized by the quantization operation to adapt to the communication bandwidth limitation. S2. Design the control law and adaptive rate of the unmanned ship. The control law uses sliding mode control to handle the uncertainty and external disturbance of the unmanned ship system, and the adaptive rate is used to estimate unknown parameters. S3. By utilizing a time-varying threshold event triggering mechanism, the event triggering mechanism is combined with sliding mode control to construct a quantitative feedback control system. The time-varying threshold event triggering mechanism dynamically adjusts the update frequency of the unmanned vessel trajectory based on the state error. S4. Prove the stability of the quantized feedback control system based on Lyapuno theory.

2. The event-driven adaptive trajectory tracking control method for unmanned vessels according to claim 1, characterized in that, Step S1 specifically includes: S11. Construct the kinematic and dynamic models of the USV as follows: , In the formula, Let be the transformation matrix. The inertia matrix, For the Coriolis and centripetal force matrices, Here is the damping matrix; , The location of the unmanned vessel. For the bow angle of the unmanned vessel; , These are the forward speed, lateral speed, and bow angular velocity of the unmanned vessel. To control the quantization value of the input, , , , , , , , , , , , For the quality of USV, For added mass, For the center of gravity of the USV, Let be the moment of inertia about the vertical axis. These are fluid dynamic coefficients; S12, Control input Quantization is performed using a uniform quantizer; the result is... ,in To quantify the level; S13, Assuming external interference , , , , The upper limit of unknown external time-varying disturbance variables, all of which are greater than 0; assume an ideal reference trajectory. It is continuously differentiable, and and its differential term Both are bounded; S14, Order ,definition: , In the formula , It is time-varying and unknown. , As its symbol is obtained as a constant throughout the quantization process, due to , Bounded; hour, Bounded, therefore .

3. The event-driven adaptive trajectory tracking control method for unmanned vessels according to claim 2, characterized in that, Step S2 specifically includes: S21. Define the uncertainty term Then the kinematic and dynamic model of the USV simplifies to: , S22. Define the trajectory tracking error and its derivative. Taking the derivative of the trajectory tracking error again, we get: = ; S23, Define the sliding surface ,in ;but Sliding surface As a core indicator for error monitoring in trajectory control, when When the value is close to 0, it means that the USV is not only close to the desired trajectory at its current position, but also that the rate of change of the deviation is converging, which can ensure the stability of trajectory tracking from a dynamic trend perspective. S24. Combining step S14, we obtain: ; S25. According to Young's inequality: Assuming , ,because It is a rotation matrix. It is a symmetric positive definite matrix, therefore , , Therefore, we can conclude that: ； S26, combined with S24 and S15, shows that ,so Combining S24 and S25, we get ,in It is a normal number, let ,but ; S27. Using time-varying parameters express The lower limit and intermediate control signal are designed as follows: Adaptability rate ,in All parameters are positive; parameter estimation error ,in For parameters The adaptive estimate; The true value is obtained by adjusting... To approximate the real μ, because ,so .

4. The event-driven adaptive trajectory tracking control method for unmanned vessels according to claim 3, characterized in that, Step S3 specifically includes: S31. A time-varying threshold event triggering control strategy based on quantization, as follows: , ,in , ; This is the proportional coefficient of the time-varying trigger threshold, which takes the value of a positive real number. The trigger threshold is dynamically adjusted to balance communication resources and control precision. S32, triggered by the condition It can be seen that there exists a continuously time-varying coefficient. satisfy and Make , because ,make , ,get , because , Established, , These are auxiliary variables introduced during the derivation process, used to include intermediate control signals. Quantized control input The nonlinear inequality is transformed into a standard quadratic form, which facilitates the analysis of the constraint relationship between the two. as auxiliary variables The lower limit constant, Therefore, we get , ;make ,but , ,definition ,in .

5. The event-driven adaptive trajectory tracking control method for unmanned vessels according to claim 4, characterized in that, Step S4 specifically includes: S41. Define Lyapunov functions: The Lyapunov function V represents the sliding surface error. and parameter estimation error Integration Represents the energy of the sliding surface. The energy V reflects the error in the estimation of interference parameters. The smaller V is, the more stable the USV trajectory tracking, the more accurate the interference compensation, and the smaller the current deviation.

42. Prove the derivative of the Lyapunov function V. Negative definite, for Taking the derivative, we get: By proving the derivative Negative constant indicates that regardless of surface disturbances or signal discontinuities caused by quantization and event triggering, the USV's trajectory tracking error... Eventually, it will converge to a small neighborhood. It is the rate of change of energy on the sliding surface, as determined by the sliding mode control law. ,let and The reverse ensures that the energy of the sliding surface continues to decrease, corresponding to the dynamic convergence of the USV trajectory deviation; It is the rate of change of the disturbance compensation energy, through the adaptive law ,let The energy continues to decrease; S43, Substitution ,have to: By substitution In subsequent derivation Ensure in negative constant This allows the sliding surface energy to continuously decrease, thereby achieving dynamic convergence of the USV trajectory deviation. S44, Order , Then the inequality Established, among which ; S45, due to ,but , The simplifying variables introduced to derive Lyapunov stability. ,then Combining with step S43, we get: ; S46, Substitute adaptive rate have to By substituting the adaptive law In the derivation Negative guarantee The energy continues to decrease, making the compensation for interference increasingly precise; because ,but Thus obtain ， Combining step S45, we get: ， consider The inequalities obtained from step S44 are as follows: ， Thus obtain And thus obtain ,in , , This illustrates that when external interference occurs... Since the representation is bounded, the energy of the Lyapunov function will continue to decrease and converge to a small neighborhood. The trajectory tracking error and disturbance compensation error of the USV will stabilize within an acceptable range, thus proving that the designed quantized feedback control system can enable the unmanned surface vessel to stably track the desired trajectory. Solve the inequalities get: ,then ,Pick Much larger hour, , , , , , The obtained error will converge to a small residual, eventually remaining consistent and bounded. The convergence of the sliding surface indicates that the dynamic rate of change of the USV trajectory deviation and the current deviation are both approaching 0; When the trajectory error converges to 0, it means that the actual position and attitude of the USV will approach the desired trajectory infinitely. This indicates that the accuracy of interference compensation is approaching perfection, meaning that the estimation of the interference characteristics of wind, waves, and ocean currents is becoming increasingly accurate; S47, Yes , and Differentiating both sides yields ,therefore ;error The growth rate was The rate of change is limited, regardless of the disturbance from the waves, the ideal control command The rate of change has an upper limit. ),error It won't suddenly surge; S48. Obtained from step S27 It is continuous, and yes and The function, and It is bounded, therefore there exists a satisfying normal numbers ,because From step S47, we obtain ,thereby , That is, there exists a positive constant. satisfy ,so To prevent an unlimited number of triggers, the instruction update interval of the event-triggered mechanism has a lower limit, so as not to issue instructions frequently, while ensuring that the error is controllable and does not affect the trajectory, so that the USV can still stably track the desired route with fewer instructions.

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