Robust tracking control method for differential unmanned vehicle under actuator damage and mass change

Abstract

The invention belongs to the technical field of motion control of unmanned vehicles, and particularly relates to a robust tracking control method for a differential unmanned vehicle under actuator damage and mass change, which comprises the following steps of: 1, establishing a kinematics model and a dynamics model for the differential unmanned vehicle, qualitatively analyzing the influence of the efficiency damage and the physical quality damage of the actuator in the kinematic model and the dynamical model; 2, designing control inputs with estimated values for the left actuator and the right actuator; step 3, designing a segmented updating law for the estimator, and estimating uncertainty introduced by efficiency damage and physical quality damage of the actuator; and 4, designing a flag bit switching mechanism based on event triggering, and switching the estimator working interval. According to the method, the differential unmanned vehicle can realize stable trajectory tracking under certain actuator efficiency damage and physical quality damage.

Description

Technical Field

[0001] This invention belongs to the field of motion control technology for unmanned vehicles, specifically relating to a robust tracking control method for differential unmanned vehicles under actuator damage and mass change. Background Technology

[0002] With the rapid development of autonomous driving and intelligent unmanned system technologies, differential unmanned vehicle tracking control has become a key technology in multiple fields such as intelligent transportation, logistics and distribution, environmental monitoring, and military reconnaissance. Especially in complex open environments, such as near-shore transportation or disaster emergency response scenarios, differential unmanned vehicle systems need to possess strong anti-interference capabilities and robustness to cope with external dynamic disturbances and possible anomalies within the system. In actual operation, differential unmanned vehicles may experience a decrease in actuator efficiency or changes in physical mass due to collisions, impacts, or long-term wear, further affecting the accuracy and stability of tracking control. Therefore, there is an urgent need for a robust control method that can maintain tracking performance even under system damage and external disturbances, in order to improve the applicability and reliability of differential unmanned vehicle systems in real-world environments.

[0003] Currently, a large amount of research has been dedicated to the design of tracking control algorithms for intelligent autonomous vehicles. However, most existing methods are based on the ideal assumptions of an accurate system model and fully effective actuators, failing to adequately consider the issue of maintaining tracking performance under conditions such as decreased actuator efficiency and changes in physical mass after physical damage. Especially for typical systems like differential autonomous vehicles, whose dynamic models are relatively complex and subject to underactuated constraints, traditional control methods often cannot effectively compensate for actuator performance degradation or changes in mass parameters, leading to decreased tracking accuracy or even system instability. Furthermore, although some studies have attempted to introduce disturbance observers or adaptive control strategies to address system uncertainties, these methods typically require additional sensors or hardware support, increasing system complexity and cost, and limiting their practical application.

[0004] To address the shortcomings of the existing technologies, this invention proposes a robust tracking control method for differential autonomous vehicles under actuator performance and physical quality impairments. This method can maintain tracking stability and accuracy even when the differential autonomous vehicle system suffers uncertain damage, further improving the robustness and practicality of the differential autonomous vehicle system in real-world scenarios. Summary of the Invention (a) Technical problems to be solved The technical problem this invention aims to solve is: to address the aforementioned application requirements, a robust tracking control method for differential unmanned vehicles (UAVs) under conditions of actuator damage and mass change is needed. Specifically, considering a class of differential UAVs, and addressing the situation where they suffer a certain degree of impact damage during operation, leading to reduced actuator efficiency and changes in physical mass, a robust tracking control method for differential UAVs resistant to damage is proposed. This method does not rely on additional hardware equipment. By designing a control strategy with online estimation and compensation capabilities, it achieves dynamic compensation for the decrease in actuator efficiency and changes in mass parameters caused by impact. It can effectively overcome the shortcomings of current differential UAV tracking control methods in complex impact and collision environments, and has strong application value in engineering applications.

[0005] (II) Technical Solution To address the aforementioned technical problems, this invention provides a robust tracking control method for differential unmanned vehicles under actuator damage and mass changes, the method comprising: The first step is to establish kinematic and dynamic models for differential unmanned vehicles, and to qualitatively analyze the impact of actuator performance impairment and physical mass impairment on the kinematic and dynamic models. The second step is to design control inputs with estimated values ​​for the left and right actuators; The third step is to design a piecewise update law for the estimator to estimate the uncertainties introduced by the actuator performance damage and physical quality damage. The fourth step is to design an event-triggered flag switching mechanism to switch the estimator's working range.

[0006] In the first step, the kinematic and dynamic models for the differential-driven autonomous vehicle are established as follows: in, and It refers to the position of the differential-driven autonomous vehicle in the plane. It is the yaw angle of the differential-driven autonomous vehicle. and These are linear velocity and angular velocity, respectively. and These are the rotational speeds of the left and right wheels, respectively. and These are the control inputs for the left and right actuators, respectively. and These are the actuator efficiency coefficients, and These are the load weights driven by the left and right actuators, respectively. and These are the unknown constants related to the Coriolis force. and These are unknown nonlinear constants related to physical mass; the qualitative analysis of the impact of actuator performance impairment and physical mass impairment on the kinematic and dynamic models is as follows: Physical mass impairment affects the actuator's load weight and friction coefficient, and is therefore reflected in the parameters. , , and The main manifestation of actuator performance impairment is the actuator efficiency coefficient. and .

[0007] In the second step, the control inputs for the left and right actuators are designed as follows: in, These are positive controller parameters. , , , and , , , They are respectively for , , , as well as , , , The estimated value, Representing variables Regarding time The derivative, Representing variables Regarding time The derivative, and These are positive controller parameters. It is half the width of the mobile robot. It is the radius of the mobile robot's wheels. and It is a positive number. As an auxiliary variable, and For the desired position, For the desired yaw angle, , , , They represent , , , Regarding time The derivative, express right The derivative, , , They represent , , right The derivative of .

[0008] In the third step, a segmented estimator is designed to estimate the uncertainties introduced by actuator performance impairment and physical quality impairment, as detailed below: in, To estimate the flag bit, Indicates the number of segments to divide the interval. , , , , , , , , , , ... , ... , ... , ... These are positive estimator parameters. Representing variables of Power of 1.

[0009] In the fourth step, an event-triggered flag switching mechanism is designed to switch the estimator's working range, as follows: in, , ... It is a constant threshold. It is a time variable Error variables , , , , and state variables , A trigger function that takes a scalar value as input and outputs a scalar value.

[0010] (III) Beneficial Effects Compared with the prior art, the beneficial effects of the present invention are: (1) The differential unmanned vehicle tracking control method proposed in this invention, which is resistant to actuator performance damage and physical quality damage, can enable the differential unmanned vehicle to achieve stable trajectory tracking under certain actuator performance damage and physical quality damage. (2) The present invention designs a segmented estimator and an event-triggered flag switching mechanism, which can dynamically adjust the estimation and fitting characteristics of the estimator according to the real-time performance of the system, thereby improving the ability to compensate for actuator performance damage and physical quality damage. (3) The present invention can reduce the adverse effects of disturbances on tracking error by adjusting the controller parameters and estimator parameters. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0012] Figure 2 This is a schematic diagram of the tracking error in the x-direction.

[0013] Figure 3 This is a schematic diagram of the tracking error in the y-direction.

[0014] Figure 4 This is a schematic diagram of the tracking error in the z-direction.

[0015] Figure 5 This is a schematic diagram for controlling the input.

[0016] Figure 6 This is a schematic diagram of the estimator's estimated values.

[0017] Figure 7 This is a diagram illustrating the flags that trigger the event. Detailed Implementation

[0018] To make the objectives, contents, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0019] Example 1 This embodiment provides a differential unmanned vehicle tracking control method that resists actuator performance impairment and physical quality impairment. The system structure diagram is as follows. Figure 1 As shown, it includes the following steps: The first step is to establish a kinematic model and a dynamic model for the differential unmanned vehicle, and to qualitatively analyze the impact of actuator performance impairment and physical mass impairment on the kinematic model and the dynamic model. The second step is to design control inputs with estimated values ​​for the left and right actuators; The third step is to design a piecewise update law for the estimator to estimate the uncertainties introduced by the actuator performance damage and physical quality damage. The fourth step is to design an event-triggered flag switching mechanism to switch the estimator's working range.

[0020] Furthermore, in the first step, the kinematic and dynamic models for the differential-driven autonomous vehicle are established as follows: in, , , , , , , , The qualitative analysis of the impact of actuator performance impairment and physical mass impairment on the kinematic and dynamic models is as follows: Physical mass impairment mainly affects the actuator's load weight and friction coefficient, and therefore is primarily reflected in the parameters. , , and The impairment of actuator performance is mainly reflected in the actuator efficiency coefficient. and .

[0021] In the second step, the control inputs for the left and right actuators are designed as follows: in, These are positive controller parameters. and These are positive controller parameters. , , , , In the third step, a segmented estimator is designed to estimate the uncertainties introduced by actuator performance impairment and physical quality impairment, as follows: in, , , , , , , , , , , , .

[0022] In the fourth step, an event-triggered flag switching mechanism is designed to switch the estimator's working range, as follows: in, The proposed method was verified through simulation using MATLAB. The differential equation was solved numerically using the fourth-order Runge-Kutta method, with a calculation step size of 0.005 seconds and a simulation duration of 60 seconds. Physical mass damage occurred at the 20th second, namely: At the 40th second, actuator performance damage occurs, namely: Simulation results are attached. Figures 2-7 As shown.

[0023] in, Figure 3 and Figure 4 The diagram shows the tracking error generated by the proposed method. It can be seen that after being disturbed at the 10th and 30th seconds, the controller still ensures that the unknown tracking error converges quickly. Figure 5 and Figure 6 This is a schematic diagram illustrating the tracking error of the proposed method without introducing perturbation estimates. (Comparison) Figure 3 and Figure 4 It can be seen that not introducing the disturbance estimate in the second step will lead to a significant increase in tracking error under disturbance. Figure 7 It is to reduce the control parameters in the third step. and The following is a schematic diagram of the tracking error. , Compared to the original parameters, both parameters have decreased. Figure 3 and Figure 4 The comparison shows that the parameters and As the disturbance is reduced, its impact on tracking performance increases.

[0024] The simulation results above verify the effectiveness of this method and its beneficial effects.

[0025] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A robust tracking control method for differential unmanned vehicles under actuator damage and mass change, characterized in that, The method includes: The first step is to establish kinematic and dynamic models for differential unmanned vehicles, and to qualitatively analyze the impact of actuator performance impairment and physical mass impairment on the kinematic and dynamic models. The second step is to design control inputs with estimated values ​​for the left and right actuators; The third step is to design a piecewise update law for the estimator to estimate the uncertainties introduced by the actuator performance damage and physical quality damage. The fourth step is to design an event-triggered flag switching mechanism to switch the estimator's working range.

2. The robust tracking control method for differential unmanned vehicles under actuator damage and mass change as described in claim 1, characterized in that, In the first step, the kinematic and dynamic models for the differential-driven autonomous vehicle are established as follows: in, and It refers to the position of the differential-driven autonomous vehicle in the plane. It is the yaw angle of the differential-driven autonomous vehicle. and These are linear velocity and angular velocity, respectively. and These are the rotational speeds of the left and right wheels, respectively. and These are the control inputs for the left and right actuators, respectively. and These are the actuator efficiency coefficients, and These are the load weights driven by the left and right actuators, respectively. and These are the unknown constants related to the Coriolis force. and These are unknown nonlinear constants related to physical mass; the qualitative analysis of the impact of actuator performance impairment and physical mass impairment on the kinematic and dynamic models is as follows: Physical mass impairment affects the actuator's load weight and friction coefficient, and is therefore reflected in the parameters. , , and The main manifestation of actuator performance impairment is the actuator efficiency coefficient. and .

3. The robust tracking control method for differential unmanned vehicles under actuator damage and mass change as described in claim 2, characterized in that, In the second step, the control inputs for the left and right actuators are designed as follows: in, These are positive controller parameters. , , , and , , , They are respectively for , , , as well as , , , The estimated value, Representing variables Regarding time The derivative, Representing variables Regarding time The derivative, and These are positive controller parameters. It is half the width of the mobile robot. It is the radius of the mobile robot's wheels. and It is a positive number. As an auxiliary variable, and For the desired position, For the desired yaw angle, , , , They represent , , , Regarding time The derivative, express right The derivative, , , They represent , , right The derivative of .

4. The robust tracking control method for differential unmanned vehicles under actuator damage and mass change as described in claim 3, characterized in that, In the third step, a segmented estimator is designed to estimate the uncertainties introduced by actuator performance impairment and physical quality impairment, as follows: in, To estimate the flag bit, Indicates the number of segments to divide the interval. , , , , , , , , , , ... , ... , ... , ... These are positive estimator parameters. Representing variables of Power of 1.

5. The robust tracking control method for differential unmanned vehicles under actuator damage and mass change as described in claim 4, characterized in that, In the fourth step, an event-triggered flag switching mechanism is designed to switch the estimator's working range, as follows: in, , ... It is a constant threshold. It is a time variable Error variables , , , , and state variables , A trigger function that takes a scalar value as input and outputs a scalar value.

6. The robust tracking control method for differential unmanned vehicles under actuator damage and mass change as described in claim 1, characterized in that, The method described belongs to the field of motion control technology for unmanned vehicles.

7. The robust tracking control method for differential unmanned vehicles under actuator damage and mass change as described in claim 1, characterized in that, The method does not rely on additional hardware devices. By designing a control strategy with online estimation and compensation capabilities, it achieves dynamic compensation for the decrease in actuator efficiency and changes in mass parameters caused by shocks.

8. The robust tracking control method for differential unmanned vehicles under actuator damage and mass change as described in claim 1, characterized in that, The proposed method can effectively compensate for the shortcomings of current differential unmanned vehicle tracking control methods in complex impact and collision environments, and has strong application value in engineering applications.

9. The robust tracking control method for differential unmanned vehicles under actuator damage and mass change as described in claim 1, characterized in that, The method described above enables differential unmanned vehicles to achieve stable trajectory tracking under certain actuator performance and physical quality impairments.

10. The robust tracking control method for differential unmanned vehicles under actuator damage and mass change as described in claim 1, characterized in that, The method described above can reduce the adverse effects of disturbances on tracking errors by adjusting controller parameters and estimator parameters.

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