Nonlinear Neuro-Optimal Tracking Control via Stable Iterative Q-Learning Algorithm

2017 ◽  
pp. 111-131 ◽  
Author(s):  
Qinglai Wei ◽  
Ruizhuo Song ◽  
Benkai Li ◽  
Xiaofeng Lin
Keyword(s):  
Tracking Control ◽  
Learning Algorithm ◽  
Q Learning ◽  
Optimal Tracking ◽  
Optimal Tracking Control

Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm

2015 ◽  
Vol 168 ◽  
pp. 520-528 ◽  
Author(s):  
Qinglai Wei ◽  
Ruizhuo Song ◽  
Qiuye Sun
Keyword(s):  
Tracking Control ◽  
Learning Algorithm ◽  
Q Learning ◽  
Optimal Tracking ◽  
Optimal Tracking Control

scholarly journals Online Optimal Control of Robotic Systems with Single Critic NN-Based Reinforcement Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiaoyi Long ◽  
Zheng He ◽  
Zhongyuan Wang
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Tracking Error ◽  
Adaptive Dynamic Programming ◽  
Robotic Systems ◽  
Control Synthesis ◽  
Optimal Tracking ◽  
Optimal Tracking Control

This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)-based reinforcement learning (RL) method. To this end, we rewrite the robotic system model as a state-space form, which will facilitate the realization of optimal tracking control synthesis. To maintain the tracking response, a steady-state control is designed, and then an adaptive optimal tracking control is used to ensure that the tracking error can achieve convergence in an optimal sense. To solve the obtained optimal control via the framework of adaptive dynamic programming (ADP), the command trajectory to be tracked and the modified tracking Hamilton-Jacobi-Bellman (HJB) are all formulated. An online RL algorithm is the developed to address the HJB equation using a critic NN with online learning algorithm. Simulation results are given to verify the effectiveness of the proposed method.

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