An adaptive-based unmanned surface vehicle path following control method

Abstract

This invention provides an adaptive control method for unmanned surface vessels (USVs) based on parameter identification. The method measures the sway velocity and heading angle signals of the USV using an inertial navigation device. Based on the structure of the USV system, its interference parameters are categorized into three types: constant interference, translational-rotational coupling interference, and translational interference. Each type of interference is then combined using both inertial delay and integral iteration methods for adaptive parameter identification. The approximate differential signal of the velocity error is then solved based on the inertial delay and the nonlinear signal of the sway velocity error. Finally, the combined adaptive identification signal of the three types of interference, the sway velocity error signal, and the velocity error combined integral signal are superimposed to obtain the final sway thrust signal, achieving precise tracking control of the USV's desired velocity. The advantages of this method are that it does not require precise information about the USV's structure and interference parameters, and it exhibits high dynamic performance in velocity control.

Description

Technical Field

[0001] This invention relates to the fields of sway propulsion, trajectory tracking and maneuvering control of unmanned surface vessels (USVs), and more specifically, to an adaptive USV trajectory control method. Background Technology

[0002] Traditional unmanned surface vessel (USV) trajectory tracking control algorithms primarily employ PID control. However, due to the nonlinearity, time delay, uncertainty, and interference from complex marine environments inherent in USV motion, coupled with numerous uncertainties in the USV's mathematical model, PID control suffers from limitations such as poor dynamic performance, insufficient anti-interference capability, and low trajectory accuracy. Therefore, in recent years, modern control methods such as fuzzy control, adaptive control, inversion control, and robust control have been increasingly applied to USV trajectory tracking control. Based on these background factors, this invention proposes a method combining error feedback, sliding mode control, and adaptive control to achieve velocity stability and trajectory tracking, thereby improving the dynamic performance of USV control and giving the invention significant theoretical and engineering practical value.

[0003] It should be noted that the information in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive unmanned surface vessel (USV) trajectory control method, thereby overcoming the problems of insufficient stability and accuracy of USV trajectory tracking control caused by defects in related technologies.

[0005] According to one aspect of the present invention, an adaptive unmanned surface vessel trajectory control method is provided, comprising the following six steps: Step S10: Install an inertial navigation device on the unmanned surface vessel (USV) to measure its sway velocity signal. Based on the USV's mission, set the desired sway velocity signal and compare it to obtain the USV's sway velocity error signal. Then, integrate the signals to obtain the integrated sway velocity error signal. Next, design a second-order oscillating differential filter to obtain the second-order approximate differential signal of the USV's sway velocity error. Finally, combine the USV's sway velocity error signal, the integrated sway velocity error signal, and the second-order approximate differential signal to obtain the USV's sway velocity error sliding mode signal.

[0006] Step S20: Based on the desired sway velocity signal of the unmanned surface vessel (USV), a steady sway thrust signal is designed; then, based on the sway velocity error sliding mode signal and the sway velocity error signal of the USV, an adaptive compensation rate signal for sway thrust error is set; then, integration is performed to obtain the adaptive compensation signal for sway thrust error; then, based on the sway velocity error sliding mode signal of the USV, a sliding mode adaptive compensation rate signal for sway thrust is designed, and then integration is performed to obtain the sliding mode adaptive compensation signal for sway thrust; then, nonlinear transformation is performed on the sway velocity error sliding mode signal of the USV to obtain the sway velocity error sliding mode nonlinear signal; finally, the steady sway thrust signal, the sway velocity error sliding mode signal, the sway velocity error sliding mode nonlinear signal, the adaptive compensation signal for sway thrust error, and the sliding mode adaptive compensation signal for sway thrust are superimposed to obtain the final sway thrust signal of the USV.

[0007] Step S30: Install an inertial navigation device on the unmanned surface vessel (USV) to measure its lateral position signal. Then, based on the USV's trajectory control task, set the desired lateral position signal and compare it to obtain the USV's lateral position error signal. Then, perform integration to obtain the USV's lateral position error integral signal. Then, design a second-order oscillating differential filter to obtain the USV's second-order approximate differential signal of lateral position error. Finally, superimpose the USV's lateral position error signal and the USV's lateral position error integral signal to obtain the USV's heading angle desired signal.

[0008] Step S40: Install an inertial navigation device on the unmanned surface vessel (USV) to measure the USV's heading angle signal. Then, based on the expected heading angle signal, compare it to obtain the USV's heading angle error signal. Then, perform integration to obtain the USV's heading angle error integral signal. Then, design a second-order oscillating differential filter to obtain the USV's heading angle error second-order approximate differential signal. Finally, superimpose the USV's heading angle error signal and the USV's heading angle error integral signal to form the USV's heading angle error sliding mode signal.

[0009] Step S50: Based on the unmanned surface vessel (USV) bow angle error signal and the USV bow angle error sliding mode signal, an adaptive compensation rate signal for trajectory control error is designed and then integrated to obtain an adaptive compensation signal for trajectory control. Next, based on the USV bow angle error sliding mode signal, an adaptive compensation rate signal for trajectory control sliding mode is designed and then integrated to obtain an adaptive compensation signal for trajectory control sliding mode. Then, a nonlinear transformation is performed on the USV bow angle error sliding mode signal to obtain a nonlinear bow angle error sliding mode signal. Finally, the USV bow angle error sliding mode signal, the nonlinear bow angle error sliding mode signal, the adaptive compensation signal for trajectory control, and the adaptive compensation signal for trajectory control sliding mode are combined and superimposed to obtain the final USV bow turning torque signal, thereby enabling the USV to adaptively track the desired lateral position signal.

[0010] In one exemplary embodiment of the present invention, an inertial navigation device is installed on the unmanned surface vessel (USV) to measure the USV's sway velocity signal. Based on the USV's mission, a desired sway velocity signal is set, and a comparison is performed to obtain the USV's sway velocity error signal. This is then integrated to obtain the USV's sway velocity error integral signal. A second-order oscillating differential filter is then designed to obtain the USV's sway velocity error second-order approximate differential signal. Finally, the USV's sway velocity error sliding mode signal is obtained by combining the USV's sway velocity error signal, the USV's sway velocity error integral signal, and the USV's sway velocity error second-order approximate differential signal. ; ; ; ; in The oscillation speed signal of the unmanned surface vessel; The desired oscillation speed signal for the unmanned surface vessel; This is the sway velocity error signal for the unmanned surface vessel; The integral signal of the sway velocity error of the unmanned surface vessel; This indicates integration over time; The differential operator is the transfer function of the second-order oscillatory differential filter; The second-order approximate differential signal of the sway velocity error of the unmanned surface vessel; These are the constant parameters of the second-order oscillatory differential filter; The damping ratio parameter of the second-order oscillating differential filter. The oscillation frequency parameter is the second-order oscillating differential filter. The sliding mode signal for the sway velocity error of the unmanned surface vessel; , These are constant parameters for the sliding mode of the sway velocity error of the unmanned surface vessel.

[0011] In one exemplary embodiment of the present invention, a steady-state pitch thrust signal is designed based on the desired pitch velocity signal of the unmanned surface vessel (USV); a pitch thrust error adaptive compensation rate signal is set based on the pitch velocity error sliding mode signal and the pitch velocity error signal of the USV; then, integration is performed to obtain the pitch thrust error adaptive compensation signal; then, based on the pitch velocity error sliding mode signal of the USV, a pitch thrust sliding mode adaptive compensation rate signal is designed, and then integration is performed to obtain the pitch thrust sliding mode adaptive compensation signal; then, a nonlinear transformation is performed based on the pitch velocity error sliding mode signal of the USV to obtain a pitch velocity error sliding mode nonlinear signal; finally, the steady-state pitch thrust signal, the USV pitch velocity error sliding mode signal, the pitch velocity error sliding mode nonlinear signal, the pitch thrust error adaptive compensation signal, and the pitch thrust sliding mode adaptive compensation signal are superimposed to obtain the final USV pitch thrust signal, including: ; ; ; ; ; ; ; in For steady oscillation propulsion signal, It is a constant scaling parameter; The adaptive compensation rate signal for sway propulsion error; This is an adaptive compensation signal for sway propulsion error; The sliding mode adaptive compensation rate signal for oscillation propulsion. For the sliding mode adaptive compensation signal of the oscillation propulsion force; , This is a constant parameter signal used to adjust the rate of adaptive compensation. The parameter is a constant integral. The sliding mode nonlinear signal is the oscillation velocity error. These are constant nonlinear transformation parameters; For the final unmanned surface vessel's pitch propulsion signal, , These are constant control parameters.

[0012] In one exemplary embodiment of the present invention, an inertial navigation device is installed on the unmanned surface vessel (USV) to measure its lateral position signal. Then, based on the USV's trajectory control task, a desired lateral position signal is set, and a comparison is performed to obtain the USV's lateral position error signal. This is then integrated to obtain the integrated lateral position error signal. A second-order oscillating differential filter is then designed to obtain the second-order approximate differential signal of the USV's lateral position error. Finally, the USV's lateral position error signal and the integrated lateral position error signal are superimposed to obtain the USV's desired heading angle signal, including: ; ; ; ; in This is the lateral position signal of the unmanned surface vessel. This is the desired lateral position signal. This is the lateral position error signal for the unmanned surface vessel; This is the integral signal of the unmanned surface vessel's lateral position error; This is the second-order approximate differential signal of the unmanned surface vessel's lateral position error; This is the desired heading angle signal for the unmanned surface vessel. , , These are constant control parameters.

[0013] In one exemplary embodiment of the present invention, an inertial navigation device is installed on the unmanned surface vessel (USV) to measure the USV's heading angle signal. Then, based on the expected heading angle signal, a heading angle error signal is obtained by comparison. This error is then integrated to obtain the integrated heading angle error signal. A second-order oscillating differential filter is then designed to obtain the second-order approximate differential signal of the USV's heading angle error. Finally, the USV's heading angle error signal and the integrated heading angle error signal are superimposed to form the USV's heading angle error sliding mode signal, which includes: ; ; ; ; in This is the heading angle signal for the unmanned surface vessel. This is the bow angle error signal for the unmanned surface vessel; This is the integral signal of the bow angle error of the unmanned surface vessel; This is the second-order approximate differential signal of the bow angle error of the unmanned surface vessel; This is the sliding mode signal for the bow angle error of the unmanned surface vessel. , These are constant control parameters.

[0014] In one exemplary embodiment of the present invention, based on the unmanned surface vessel (USV) bow angle error signal and the USV bow angle error sliding mode signal, an adaptive compensation rate signal for trajectory control error is designed, and then integrated to obtain an adaptive compensation signal for trajectory control; then, based on the USV bow angle error sliding mode signal, an adaptive compensation rate signal for trajectory control sliding mode is designed; then, integrated to obtain an adaptive compensation signal for trajectory control sliding mode; then, a nonlinear transformation is performed on the USV bow angle error sliding mode signal to obtain a nonlinear signal for bow angle error sliding mode; finally, the USV bow angle error sliding mode signal, the nonlinear signal for bow angle error sliding mode, the adaptive compensation signal for trajectory control, and the adaptive compensation signal for trajectory control sliding mode are combined and superimposed to obtain the final USV bow turning torque signal, including: ; ; ; ; ; ; in The adaptive compensation rate signal for trajectory control error. For trajectory control adaptive compensation signal; For trajectory control sliding mode adaptive compensation rate signal; This is the adaptive compensation signal for sliding mode in trajectory control. , This is a constant parameter used to adjust the speed of the adaptive compensation rate for trajectory control. This is the nonlinear signal of the heading angle error sliding mode. These are constant nonlinear transformation parameters; For the final unmanned surface vessel bow torque signal, , These are constant control parameters.

[0015] Beneficial effects This invention provides an adaptive unmanned surface vessel (USV) trajectory control method, with three main innovations. First, by using a unified form of a second-order oscillating differential filter, the pitch velocity error signal and bow angle error signal are differentiated, reducing the requirements for measuring instruments, increasing system damping, and improving the stability of the USV control. Second, by using a unified form of proportional, derivative, and integral filters, the velocity error sliding mode signal and the bow angle error sliding mode signal are respectively composed, and combined with a unified adaptive compensation signal and a sliding mode nonlinear signal to form the pitch thrust and bow torque signals; this simplifies the overall control structure, facilitates parameter selection, and significantly improves the accuracy of the USV control. Third, by designing a combined adaptive compensation signal using sliding mode signals and error signals, adaptive compensation is applied to the pitch thrust and bow torque signals for trajectory control, improving the environmental adaptability and anti-interference capability of the entire USV control system.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0018] Figure 1 This is a flowchart of an adaptive unmanned surface vessel trajectory control method provided by the present invention; Figure 2 This is the pitch velocity signal curve (unit: meters per second) of the unmanned surface vessel provided by the method in the embodiment of the present invention. Figure 3 This is the sway velocity error signal curve (unit: meters per second) of the unmanned surface vessel provided by the method in the embodiment of the present invention. Figure 4 This is the second-order approximate differential signal curve of the sway velocity error of the unmanned surface vessel provided by the method of the present invention (unitless). Figure 5 This is the unmanned surface vessel pitch thrust signal curve (unit: Newtons) of the method provided in the embodiments of the present invention. Figure 6 This is the lateral position error signal curve of the unmanned surface vessel (USV) provided by the method in this embodiment of the invention (unit: meters). Figure 7 This is the lateral position signal curve (unit: meters) of the unmanned surface vessel provided by the method in the embodiments of the present invention. Figure 8 This is the heading angle signal curve (unit: degrees) of the unmanned surface vessel provided by the method in the embodiments of the present invention. Figure 9 This is the bow angle error signal curve (unit: degrees) of the unmanned surface vessel provided by the method in the embodiments of the present invention. Figure 10 This is the bow turning torque signal curve (unit: Newton-meter) of the unmanned surface vessel provided in the embodiments of the present invention. Detailed Implementation

[0019] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make the invention more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention may be practiced with one or more of these specific details omitted, or other methods, components, apparatus, steps, etc., may be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the invention.

[0020] This invention provides an adaptive unmanned surface vessel (USV) trajectory control method. It measures the USV's sway velocity, lateral position, and heading angle signals using an inertial navigation system. Based on the sway velocity error, lateral position error, and heading angle error signals, a second-order oscillating differential filter is used to obtain filtered differential signals. These are then superimposed with corresponding error integral signals to form velocity error sliding mode signals and heading angle error sliding mode signals. Adaptive compensation is then applied to the sway thrust and heading torque signals for trajectory control using adaptive design. Finally, the sway thrust and heading torque are obtained by superimposing the constant sway thrust signal with corresponding sliding mode nonlinear signals and adaptive control signals, thereby achieving tracking control of a given speed and a desired trajectory.

[0021] Below, we will combine the appendix Figure 1 The adaptive unmanned surface vessel trajectory control method of the present invention will be further explained and described. (Reference) Figure 1 As shown, this adaptive unmanned surface vessel trajectory control method includes the following steps: Step S10 can be broken down into the following four sub-steps. First, install inertial navigation equipment on the unmanned surface vessel (USV), measure its sway velocity signal, and set the desired sway velocity signal based on the USV's mission. The sway velocity error signal is then obtained through comparison, as follows: ; in The oscillation speed signal of the unmanned surface vessel; The desired oscillation speed signal for the unmanned surface vessel; This is the sway velocity error signal for the unmanned surface vessel.

[0022] In this case, select Meters per second, the final sway velocity signal curve of the unmanned surface vessel is as follows: Figure 2 As shown, the sway velocity error signal curve of the unmanned surface vessel is as follows: Figure 3 As shown.

[0023] The second step is to integrate the sway velocity error signal to obtain the integrated sway velocity error signal of the unmanned surface vessel, as follows: ; in The integral signal of the sway velocity error of the unmanned surface vessel; This indicates integration over time.

[0024] The third step is to design a second-order oscillating differential filter to obtain the second-order approximate differential signal of the unmanned surface vessel's sway velocity error as follows: ; in The differential operator is the transfer function of the second-order oscillatory differential filter; The second-order approximate differential signal of the sway velocity error of the unmanned surface vessel; These are the constant parameters of the second-order oscillatory differential filter; The damping ratio parameter of the second-order oscillating differential filter. The oscillation frequency parameter is the second-order oscillatory differential filter.

[0025] In this case, we selected , , Finally, the second-order approximate differential signal of the unmanned surface vessel's sway velocity error is obtained as follows: Figure 4 As shown.

[0026] The fourth step involves combining the unmanned surface vessel's (USV) sway velocity error signal, the USV's sway velocity error integral signal, and the USV's sway velocity error second-order approximate differential signal to obtain the USV's sway velocity error sliding mode signal as follows: ; in The sliding mode signal for the sway velocity error of the unmanned surface vessel; , Let be the constant parameter of the sliding mode for the sway velocity error of the unmanned surface vessel. In this case, we select... , .

[0027] Step S20 can be broken down into the following five sub-steps. The first step, based on the desired sway velocity signal of the unmanned surface vessel, designs the steady sway thrust signal as follows: ; in For steady oscillation propulsion signal, This is a constant scaling parameter. In this case, we select... .

[0028] The second step involves setting an adaptive compensation rate signal for the sway thrust error based on the sliding mode signal of the unmanned surface vessel's sway velocity error and the sway velocity error signal of the unmanned surface vessel; then, integration is performed to obtain the adaptive compensation signal for the sway thrust error as follows: ; ; in The adaptive compensation rate signal for sway propulsion error; For adaptive compensation signal of sway thrust error, This is a constant parameter signal used to adjust the rate of adaptive compensation. These are constant integration parameters. In this case, we select... , , .

[0029] The third step is to design an adaptive compensation rate signal for the sway thrust sliding mode based on the sway velocity error sliding mode signal of the unmanned surface vessel, and then integrate it to obtain the sway thrust sliding mode adaptive compensation signal as follows: ; ; in The sliding mode adaptive compensation rate signal for oscillation propulsion. This is the adaptive compensation signal for the oscillation propulsion sliding mode. This is a constant parameter signal used to adjust the rate of adaptive compensation.

[0030] The fourth step involves performing a nonlinear transformation on the sliding mode signal of the unmanned surface vessel's sway velocity error, resulting in the following nonlinear sliding mode signal for the sway velocity error: ; in The sliding mode nonlinear signal is the oscillation velocity error. These are constant nonlinear transformation parameters. In this case, we select... Fifth, the steady sway thrust signal, the unmanned surface vessel's sway velocity error sliding mode signal, the sway velocity error sliding mode nonlinear signal, the sway thrust error adaptive compensation signal, and the sway thrust sliding mode adaptive compensation signal are superimposed to obtain the final unmanned surface vessel sway thrust signal as follows: ; in For the final unmanned surface vessel's pitch propulsion signal, , This is a constant control parameter. In this case, we select... , The final obtained unmanned surface vessel pitch thrust signal is as follows: Figure 5 As shown.

[0031] Step S30 can be broken down into the following four sub-steps. First, install inertial navigation equipment on the unmanned surface vessel (USV) to measure its lateral position signal. Then, based on the USV's trajectory control task, set the desired lateral position signal and compare it to obtain the USV's lateral position error signal as follows: ; in This is the lateral position signal of the unmanned surface vessel. This is the desired lateral position signal. This is the lateral position error signal for the unmanned surface vessel.

[0032] In this invention example, the following is selected The final lateral position error signal of the unmanned surface vessel is as follows: Figure 6 As shown, the lateral position signal of the unmanned surface vessel is as follows: Figure 7 As shown.

[0033] The second step involves integrating the unmanned surface vessel's lateral position error signal to obtain the integrated lateral position error signal, as follows: ; in This is the integral signal of the unmanned surface vessel's lateral position error.

[0034] The third step is to design a second-order oscillating differential filter to obtain the second-order approximate differential signal of the unmanned surface vessel's lateral position error as follows: ; in This is the second-order approximate differential signal of the unmanned surface vessel's lateral position error.

[0035] Fourth step: By superimposing the unmanned surface vessel's lateral position error signal and the integrated lateral position error signal, the expected heading angle signal of the unmanned surface vessel is obtained as follows: ; in This is the desired heading angle signal for the unmanned surface vessel. , , This is a constant control parameter. In this case, we select... , , .

[0036] Step S40 can be broken down into the following four sub-steps. First, install an inertial navigation device on the unmanned surface vessel (USV) to measure its heading angle signal. Then, based on the expected heading angle signal and after comparison, obtain the USV's heading angle error signal as follows: ; in This is the heading angle signal for the unmanned surface vessel. This is the bow angle error signal of the unmanned surface vessel (USV). In this case, the final bow angle signal of the USV is as follows: Figure 8 As shown, the bow angle error signal of the unmanned surface vessel is as follows: Figure 9 As shown.

[0037] The second step involves integrating the unmanned surface vessel (USV) heading angle error signal to obtain the integrated heading angle error signal as follows: ; in This is the integral signal of the bow angle error of the unmanned surface vessel.

[0038] The third step is to design a second-order oscillating differential filter to obtain the second-order approximate differential signal of the unmanned surface vessel's bow angle error, as follows: ; in This is the second-order approximate differential signal of the bow angle error of the unmanned surface vessel.

[0039] The fourth step involves superimposing the unmanned surface vessel (USV) heading angle error signal and the integrated USV heading angle error signal to form the USV heading angle error sliding mode signal as follows: ; in This is the sliding mode signal for the bow angle error of the unmanned surface vessel. , This is a constant control parameter. In this case, we select... , .

[0040] Step S50 can be broken down into the following four sub-steps. First, based on the unmanned surface vessel (USV) bow angle error signal and the USV bow angle error sliding mode signal, design the trajectory control error adaptive compensation rate signal, and then integrate it to obtain the trajectory control adaptive compensation signal as follows: ; ; in The adaptive compensation rate signal for trajectory control error. For trajectory control adaptive compensation signal; This is a constant parameter used to adjust the speed of the adaptive compensation rate for trajectory control.

[0041] The second step is to design a trajectory control sliding mode adaptive compensation rate signal based on the unmanned surface vessel's bow angle error sliding mode signal; then, by integration, the trajectory control sliding mode adaptive compensation signal is obtained as follows: ; ; in For trajectory control sliding mode adaptive compensation rate signal; This is the adaptive compensation signal for sliding mode in trajectory control. This is a constant parameter used to adjust the speed of the adaptive compensation rate for trajectory control.

[0042] In this case, we selected , .

[0043] The third step involves performing a nonlinear transformation on the unmanned surface vessel's bow angle error sliding mode signal to obtain the following bow angle error sliding mode nonlinear signal: ; in This is the nonlinear signal of the heading angle error sliding mode. These are constant nonlinear transformation parameters. In this case, we select... .

[0044] The fourth step involves combining and superimposing the unmanned surface vessel (USV) bow angle error sliding mode signal, bow angle error sliding mode nonlinear signal, trajectory control adaptive compensation signal, and trajectory control sliding mode adaptive compensation signal to obtain the final USV bow turning torque signal as follows: ; in For the final unmanned surface vessel bow torque signal, , This is a constant control parameter. In this case, we select... , The final obtained unmanned surface vessel (USV) bow torque signal is as follows: Figure 10 As shown.

[0045] Ultimately by Figure 2 It can be seen that, at approximately 100 seconds, the speed of the unmanned surface vessel stabilizes at the desired speed of about 8 meters per second; Figure 3 It can be seen that the velocity error converges smoothly to 0 without overshoot; from Figure 5 It can be seen that the unmanned surface vessel's yaw thrust signal increases slowly and eventually becomes smooth and stable, thus perfectly meeting the requirements for engineering implementation; Figure 6 It can be seen that the lateral position curve has no overshoot or oscillation; from Figure 7 It can be seen that the lateral position error can converge to 0 in about 100 seconds, demonstrating the excellent accuracy of this invention; Figure 8 It can be seen that the maximum bow angle signal of the unmanned surface vessel does not exceed 10 degrees, which is also convenient for engineering implementation; Figure 9 It can be seen that the heading angle error signal converges quickly, stabilizing at around 0 degrees in approximately 30 seconds; this demonstrates the excellent speed of this invention. Figure 10 It can be seen that the final unmanned surface vessel's bow turning torque signal, after experiencing rapid oscillations, converges smoothly to around 0. The experimental results show that the entire trajectory control process exhibits excellent stability and speed, especially in terms of control accuracy, both in angle and position, thus giving this invention significant theoretical and engineering value.

Claims

1. An adaptive unmanned surface vessel trajectory control method, characterized in that, Includes the following steps: Step S10: Install an inertial navigation device on the unmanned surface vessel (USV) to measure its sway velocity signal. Based on the USV's mission, set the desired sway velocity signal and compare it to obtain the USV's sway velocity error signal. Integrate this signal to obtain the integrated sway velocity error signal. Then, design a second-order oscillating differential filter to obtain the second-order approximate differential signal of the USV's sway velocity error. Finally, combine the USV's sway velocity error signal, the integrated sway velocity error signal, and the second-order approximate differential signal to obtain the USV's sway velocity error sliding mode signal as follows: ； ； ； ； in The oscillation speed signal of the unmanned surface vessel; The desired oscillation speed signal for the unmanned surface vessel; This is the sway velocity error signal for the unmanned surface vessel; The integral signal of the sway velocity error of the unmanned surface vessel; This indicates integration over time; The differential operator is the transfer function of the second-order oscillatory differential filter; The second-order approximate differential signal of the sway velocity error of the unmanned surface vessel; These are the constant parameters of the second-order oscillatory differential filter; The damping ratio parameter of the second-order oscillating differential filter. The oscillation frequency parameter is the second-order oscillating differential filter. The sliding mode signal for the sway velocity error of the unmanned surface vessel; , For the constant parameters of the sliding mode of the sway velocity error of the unmanned surface vessel; Step S20: Based on the desired sway velocity signal of the unmanned surface vessel (USV), a steady sway thrust signal is designed; then, based on the sway velocity error sliding mode signal and the sway velocity error signal of the USV, an adaptive compensation rate signal for the sway thrust error is set; then, integration is performed to obtain the adaptive compensation signal for the sway thrust error; then, based on the sway velocity error sliding mode signal of the USV, a sliding mode adaptive compensation rate signal for the sway thrust is designed, and then integration is performed to obtain the sliding mode adaptive compensation signal for the sway thrust; then, nonlinear transformation is performed on the sway velocity error sliding mode signal of the USV to obtain the sway velocity error sliding mode nonlinear signal; finally, the steady sway thrust signal, the sway velocity error sliding mode signal, the sway velocity error sliding mode nonlinear signal, the adaptive compensation signal for the sway thrust error, and the sliding mode adaptive compensation signal for the sway thrust are superimposed to obtain the final sway thrust signal of the USV as follows: ； ； ； ； ； ； ； in For steady oscillation propulsion signal, It is a constant scaling parameter; The adaptive compensation rate signal for sway propulsion error; This is an adaptive compensation signal for sway propulsion error; The sliding mode adaptive compensation rate signal for oscillation propulsion. For the sliding mode adaptive compensation signal of the oscillation propulsion force; , This is a constant parameter signal used to adjust the rate of adaptive compensation. The parameter is a constant integral. The sliding mode nonlinear signal is the oscillation velocity error. These are constant nonlinear transformation parameters; For the final unmanned surface vessel's pitch propulsion signal, , These are constant control parameters.

2. The adaptive unmanned surface vessel trajectory control method according to claim 1, characterized in that, An inertial navigation system is installed on the unmanned surface vessel (USV) to measure its lateral position signal. Then, based on the USV's trajectory control task, a desired lateral position signal is set, and the result is compared to obtain the USV's lateral position error signal. This error is then integrated to obtain the integrated lateral position error signal. A second-order oscillating differential filter is then designed to obtain the second-order approximate differential signal of the USV's lateral position error. Finally, the USV's lateral position error signal and the integrated lateral position error signal are superimposed to obtain the desired heading angle signal as follows: ； ； ； ； in This is the lateral position signal of the unmanned surface vessel. This is the desired lateral position signal. This is the lateral position error signal for the unmanned surface vessel; This is the integral signal of the unmanned surface vessel's lateral position error; This is the second-order approximate differential signal of the unmanned surface vessel's lateral position error; This is the desired heading angle signal for the unmanned surface vessel. , , These are constant control parameters.

3. The adaptive unmanned surface vessel trajectory control method according to claim 2, characterized in that, An inertial navigation device is installed on the unmanned surface vessel (USV) to measure its heading angle signal. Then, based on the expected heading angle signal, a heading angle error signal is obtained by comparison. This error is then integrated to obtain the integral heading angle error signal. A second-order oscillating differential filter is designed to obtain the second-order approximate differential signal of the heading angle error. Finally, the heading angle error signal and the integral heading angle error signal are superimposed to form the sliding mode heading angle error signal as follows: ； ； ； ； in This is the heading angle signal for the unmanned surface vessel. This is the bow angle error signal for the unmanned surface vessel; This is the integral signal of the bow angle error of the unmanned surface vessel; This is the second-order approximate differential signal of the bow angle error of the unmanned surface vessel; This is the sliding mode signal for the bow angle error of the unmanned surface vessel. , These are constant control parameters.

4. The adaptive unmanned surface vessel trajectory control method according to claim 3, characterized in that, Based on the unmanned surface vessel (USV) heading angle error signal and its sliding mode signal, an adaptive compensation rate signal for trajectory control error is designed and then integrated to obtain the adaptive compensation signal for trajectory control. Next, based on the USV heading angle error sliding mode signal, an adaptive compensation rate signal for trajectory control sliding mode is designed and integrated to obtain the adaptive compensation signal for trajectory control sliding mode. Then, a nonlinear transformation is performed on the USV heading angle error sliding mode signal to obtain the heading angle error sliding mode nonlinear signal. Finally, the USV heading angle error sliding mode signal, the heading angle error sliding mode nonlinear signal, the adaptive compensation signal for trajectory control, and the adaptive compensation signal for trajectory control sliding mode are combined and superimposed to obtain the final USV bow-turning torque signal as follows: ； ； ； ； ； ； in The adaptive compensation rate signal for trajectory control error. For trajectory control adaptive compensation signal; For trajectory control sliding mode adaptive compensation rate signal; This is the adaptive compensation signal for sliding mode in trajectory control. , This is a constant parameter used to adjust the speed of the adaptive compensation rate for trajectory control. This is the nonlinear signal of the heading angle error sliding mode. These are constant nonlinear transformation parameters; For the final unmanned surface vessel bow torque signal, , These are constant control parameters.

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