Neural Network L-two-gain Robust Control for Flexible Arm Space Robot Based on Virtual Control Force Conception

2012 ◽  
Vol 48 (23) ◽  
pp. 23
Author(s):  
Jie LIANG
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Space Robot ◽  
Control Force ◽  
Virtual Control ◽  
Flexible Arm ◽  
Virtual Control Force

scholarly journals Adaptive robust control for free-floating space robot with unknown uncertainty based on neural network

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881151
Author(s):  
Zhang Wenhui ◽  
Li Hongsheng ◽  
Ye Xiaoping ◽  
Huang Jiacai ◽  
Huo Mingying
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Robust Control ◽  
Control Method ◽  
External Disturbance ◽  
Adaptive Robust Control ◽  
Lyapunov Theory ◽  
Space Robot ◽  
Neural Network Controller ◽  
The Stability

It is difficult to obtain a precise mathematical model of free-floating space robot for the uncertain factors, such as current measurement technology and external disturbance. Hence, a suitable solution would be an adaptive robust control method based on neural network is proposed for free-floating space robot. The dynamic model of free-floating space robot is established; a computed torque controller based on exact model is designed, and the controller can guarantee the stability of the system. However, in practice, the mathematical model of the system cannot be accurately obtained. Therefore, a neural network controller is proposed to approximate the unknown model in the system, so that the controller avoids dependence on mathematical models. The adaptive learning laws of weights are designed to realize online real-time adjustment. The adaptive robust controller is designed to suppress the external disturbance and compensate the approximation error and improve the robustness and control precision of the system. The stability of closed-loop system is proved based on Lyapunov theory. Simulations tests verify the effectiveness of the proposed control method and are of great significance to free-floating space robot.

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

2011 ◽  
Vol 383-390 ◽  
pp. 290-296
Author(s):  
Yong Hong Zhu ◽  
Wen Zhong Gao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Wavelet Neural Network ◽  
Adaptive Robust Control ◽  
Loop Control ◽  
Control Approach ◽  
Mimo Nonlinear Systems ◽  
Output Tracking Control ◽  
Dynamic Inverse

Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

scholarly journals Robust Control for Robotic Manipulators Base on Adaptive Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 497-502 ◽  
Author(s):  
Wenhui Zhang ◽  
Xiaoping Ye ◽  
Lihong Jiang ◽  
Fang Yamin
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Robotic Manipulators ◽  
Adaptive Neural Network
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