scholarly journals Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092461
Author(s):  
Hong Zhan ◽  
Dianye Huang ◽  
Zhaopeng Chen ◽  
Min Wang ◽  
Chenguang Yang
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Linear Quadratic Regulator ◽  
Adaptive Dynamic Programming ◽  
Environment Interaction ◽  
Linear Quadratic ◽  
Adaptive Dynamic ◽  
The Neural Network ◽  
Network Weight ◽  
The Stability

The problem of optimal tracking control for robot–environment interaction is studied in this article. The environment is regarded as a linear system and an admittance control with iterative linear quadratic regulator method is obtained to guarantee the compliant behaviour. Meanwhile, an adaptive dynamic programming-based controller is proposed. Under adaptive dynamic programming frame, the critic network is performed with radial basis function neural network to approximate the optimal cost, and the neural network weight updating law is incorporated with an additional stabilizing term to eliminate the requirement for the initial admissible control. The stability of the system is proved by Lyapunov theorem. The simulation results demonstrate the effectiveness of the proposed control scheme.

scholarly journals Attitude Control with Auxiliary Structure Based on Adaptive Dynamic Programming for Reentry Vehicles

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Xu Li ◽  
Zhongtao Cheng ◽  
Bo Wang ◽  
Yongji Wang ◽  
Lei Liu
Keyword(s):  
Dynamic Programming ◽  
Attitude Control ◽  
Control Method ◽  
Sliding Mode ◽  
Adaptive Dynamic Programming ◽  
Selection Strategy ◽  
Adaptive Dynamic ◽  
Time Scale Separation ◽  
Reentry Vehicles ◽  
The Stability

This paper presents an attitude control scheme combined with adaptive dynamic programming (ADP) for reentry vehicles with high nonlinearity and disturbances. Firstly, the nonlinear attitude dynamics is divided into inner and outer loops according to the time scale separation and the cascade control principle, and a general sliding mode control method is employed to construct the main controllers for the double loops. Considering the shortage of main controllers in handling nonlinearity and sudden disturbances, an ADP structure is introduced into the outer attitude loop as an auxiliary. And the ADP structure utilizes neural network estimators to minimize the cost function and generate optimal signals through online learning, so as to compensate defect of the main controllers’ adaptability speed and accuracy. Then, the stability is analyzed by the Lyapunov method, and the parameter selection strategy of the ADP structure is derived to guide implementation. In addition, this paper puts forward skills to speed up ADP training. Finally, simulation results show that the control strategy with ADP possesses stronger adaptability and faster response than that without ADP for the nonlinear vehicle system.

Robust adaptive dynamic programming based online tracking control algorithm for real wheeled mobile robot with omni-directional vision system

2016 ◽  
Vol 39 (6) ◽  
pp. 832-847 ◽  
Author(s):  
Nguyen Tan Luy
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Mobile Robot ◽  
System Dynamics ◽  
Vision System ◽  
Dynamic Programming Algorithm ◽  
Wheeled Mobile Robot ◽  
Adaptive Dynamic Programming ◽  
Adaptive Dynamic ◽  
Robust Adaptive

This paper proposes a new method to design an online robust adaptive dynamic programming algorithm (RADPA) for a wheeled mobile robot which is equipped with an omni-directional vision system. To integrate kinematic and dynamic controllers into the unique controller, we transform the strict feedback system dynamics into tracking error dynamics. Then, we propose a control scheme which uses only one neural network rather than three proposed in the actor-critic-based control schemes for the two-player zero-sum game problem. A neural network weight update law is designed for approximating the solution of the Hamilton–Jacobi–Isaacs equation without knowing knowledge of internal system dynamics. To implement the scheme, we propose the online RADPA, in which control and disturbance laws are updated simultaneously in an iterative loop. The convergence and stability of the online RADPA are proven by Lyapunov techniques. Simulations and experiments on a wheeled mobile robot testbed are carried out to verify the effectiveness of the proposed algorithm.

scholarly journals Adaptive dynamic programming-based feature tracking control of visual servoing manipulators with unknown dynamics

2021 ◽  
Author(s):  
Xiaolin Ren ◽  
Hongwen Li
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Cost Function ◽  
Tracking Control ◽  
Feature Tracking ◽  
Visual Servoing ◽  
Error Control ◽  
Lyapunov Theory ◽  
Adaptive Dynamic Programming ◽  
Adaptive Dynamic

AbstractThis paper investigates a feature tracking control method for visual servoing (VS) manipulators adaptive dynamic programming (ADP)-based the unknown dynamics. The major superiority of ADP-based optimal control lies in that the visual tracking problem is converted to the feature tracking error control with optimal cost function. Moreover, an adaptive neural network observer is developed to approximate the entire uncertainties, which are utilized to construct an improved cost function. By establishing a critic neural network, the Hamilton–Jacobi–Bellman (HJB) equation is solved, and the approximate optimal error control policy is derived. The closed-loop VS manipulator system is verified to be ultimately uniformly bounded with the developed ADP-based feature tracking control strategy according to the Lyapunov theory. Finally, simulation results under various situations demonstrate that the proposed method achieves higher tracking accuracy than other methods, as well as satisfies energy optimal requirements.

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