scholarly journals Robust Prescribed Performance Control of Uncertain Vehicle Lateral System under State Constraints

2021 ◽  
Vol 2083 (3) ◽  
pp. 032029
Author(s):  
Jing Yu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Lyapunov Function ◽  
Finite Time ◽  
Tracking Control ◽  
State Constraints ◽  
Tracking Error ◽  
Lateral Control ◽  
Barrier Lyapunov Function ◽  
The Neural Network

Abstract In the study of the zero-error tracking control problem for vehicle lateral control systems under full-state constraints and nonparametric uncertainties, the zero-error tracking control problem is presented in this paper. A neural adaptive tracking control scheme is proposed by combining the error transformation of the vehicle lateral control system with the barrier Lyapunov function, which realizes that the tracking error of the vehicle lateral control converges to a prescribed compact set at a controllable or specified convergence rate in a specified finite time. The scheme has the following significant characteristics: 1) Based on the Nussbaum gain, the preset new energy finite-time control algorithm, the tracking error of the vehicle lateral control system with non-parametric uncertainty and external disturbance decreases to zero with t → ∞. In addition, it also has the control ability to cope with the presence or even unknown moment of inertia of the system. 2) Barrier Lyapunov function (BLF) ensures the bounded input of the neural network during the whole system envelope, and ensures the stable learning and approximation of the neural network. Furthermore, the bounded stability of the closed-loop system is proved by Lyapunov analysis. Finally, the effectiveness and superiority of the proposed control method are verified by simulation.

scholarly journals Robust Tracking Control of the Euler–Lagrange System Based on Barrier Lyapunov Function and Self-Structuring Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yi Wang ◽  
He Ma ◽  
Weidong Wu
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Lyapunov Function ◽  
Tracking Control ◽  
Tracking Error ◽  
Robust Tracking ◽  
Robust Tracking Control ◽  
Barrier Lyapunov Function ◽  
Control Scheme ◽  
Unknown Disturbances

This article studies the robust tracking control problems of Euler–Lagrange (EL) systems with uncertainties. To enhance the robustness of the control systems, an asymmetric tan-type barrier Lyapunov function (ATBLF) is used to dynamic constraint position tracking errors. To deal with the problems of the system uncertainties, the self-structuring neural network (SSNN) is developed to estimate the unknown dynamics model and avoid the calculation burden. The robust compensator is designed to estimate and compensate neural network (NN) approximation errors and unknown disturbances. In addition, a relative threshold event-triggered strategy is introduced, which greatly saves communication resources. Under the proposed robust control scheme, tracking behavior can be implemented with disturbance and unknown dynamics of the EL systems. All signals in the closed-loop system are proved to be bounded by stability analysis, and the tracking error can converge to the neighborhood near the origin. The numerical simulation results show the effectiveness and the validity of the proposed robust control scheme.

scholarly journals Attitude Controller Design with State Constraints for Kinetic Kill Vehicle Based on Barrier Lyapunov Function

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Tao Zhang ◽  
Jiong Li ◽  
Huaji Wang ◽  
ChuHan Xiao ◽  
Wanqi Li
Keyword(s):  
Lyapunov Function ◽  
State Constraints ◽  
Sliding Mode ◽  
Tracking Error ◽  
Time Derivative ◽  
Continuous Control ◽  
Side Window ◽  
Barrier Lyapunov Function ◽  
Control Command ◽  
Attitude Controller

An adaptive attitude controller is designed based on Barrier Lyapunov Function (BLF) to meet the state constraints caused by side window detection. Firstly, the attitude controller is designed based on the BLF, but the stabilization function is complex and its time derivative will cause “differential explosion”. Therefore, Finite-time-convergent Differentiator (FD) is used to estimate the first derivative of the stabilization function. If the tracking error is outside the BLF's convergence domain, BLF controller cannot guarantee the error global convergence. Sliding mode controller (SMC) is used to make the system's error converge to set domain, and then the BLF controller could be used to ensure that the output constraint is not violated. Uncertainties and unknown time-varying disturbances usually make the control precision worse and Nonlinear Disturbance Observer (NDO) is designed for estimation and compensation uncertainties and disturbances. The pseudo rate modulator (PSR) is used to shape the continuous control command to pulse or on-off signals to meet the requirements of the thruster. Numerical simulations show that the proposed method can achieve state constraints, pseudo-linear operation, and high accuracy.

scholarly journals Neural network adaptive command filtered control of robotic manipulators with input saturation

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989477
Author(s):  
Lin Wang ◽  
Chunzhi Yang
Keyword(s):  
Neural Network ◽  
Finite Time ◽  
Tracking Error ◽  
Input Saturation ◽  
Robotic Manipulators ◽  
Small Region ◽  
Network Control ◽  
External Disturbances ◽  
Time Control ◽  
The Neural Network

This paper investigates finite-time control of uncertain robotic manipulators with external disturbances by means of neural network control and backstepping technique. To solve the “explosion of terms” in traditional backstepping control, a second-order command filter is designed, and the virtual input and its first-order derivative can be obtained accurately in a finite time. The parameters of the neural network are updated by using the tracking error signals. The proposed controller can guarantee that the tracking error converges to a small region of the origin in some finite time. Finally, we give a simulation study to show the effectiveness of the proposed method.

Development of hysteresis friction model for a precise tracking control system

2011 ◽  
Vol 226 (5) ◽  
pp. 1338-1344 ◽  
Author(s):  
J Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Tracking Control ◽  
Sliding Mode ◽  
Back Propagation ◽  
Friction Model ◽  
Ball Screw ◽  
Preisach Model ◽  
The Neural Network ◽  
Hysteresis Friction

Hysteresis friction model for a tracking control system of the mechanical system was proposed in this article. Hysteresis phenomena within a microscopic displacement range were verified through experiments, and Preisach model was applied to the proposed friction model. To implement the Preisach model, the neural network algorithm was used to replace the distribute function of Preisach model. A weighing vector of the neural network was updated through the back-propagation learning method. The performance of the proposed hysteresis friction model was evaluated through the tracking control of a ball-screw system. In order to design the tracking control system, sliding mode controller was designed and the proposed friction model was applied as a feed-forward term of controller.

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