scholarly journals Variable gain gradient descent-based reinforcement learning for robust optimal tracking control of uncertain nonlinear system with input constraints

2022 ◽  
Author(s):  
Amardeep Mishra ◽  
Satadal Ghosh
Keyword(s):  
Reinforcement Learning ◽  
Nonlinear System ◽  
Tracking Control ◽  
Gradient Descent ◽  
Input Constraints ◽  
Uncertain Nonlinear System ◽  
Optimal Tracking ◽  
Variable Gain ◽  
Optimal Tracking Control

scholarly journals Deep reinforcement learning based finite-horizon optimal tracking control for nonlinear system

2018 ◽  
Vol 51 (25) ◽  
pp. 257-262 ◽  
Author(s):  
Jong Woo Kim ◽  
Byung Jun Park ◽  
Haeun Yoo ◽  
Jay H. Lee ◽  
Jong Min Lee
Keyword(s):  
Reinforcement Learning ◽  
Nonlinear System ◽  
Tracking Control ◽  
Finite Horizon ◽  
Optimal Tracking ◽  
Optimal Tracking Control

Neuro-Optimal Tracking Control for Continuous Stirred Tank Reactor With Input Constraints

2019 ◽  
Vol 15 (8) ◽  
pp. 4516-4524 ◽  
Author(s):  
Wei Zhou ◽  
Huachao Liu ◽  
Haibo He ◽  
Jun Yi ◽  
Taifu Li
Keyword(s):  
Tracking Control ◽  
Stirred Tank ◽  
Input Constraints ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Optimal Tracking ◽  
Tank Reactor ◽  
Optimal Tracking Control ◽  
Continuous Stirred Tank

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.

Reinforcement -learning for optimal tracking control of linear discrete-time systems with unknown dynamics

Automatica ◽  
2014 ◽  
Vol 50 (4) ◽  
pp. 1167-1175 ◽  
Author(s):  
Bahare Kiumarsi ◽  
Frank L. Lewis ◽  
Hamidreza Modares ◽  
Ali Karimpour ◽  
Mohammad-Bagher Naghibi-Sistani
Keyword(s):  
Reinforcement Learning ◽  
Discrete Time ◽  
Tracking Control ◽  
Optimal Tracking ◽  
Optimal Tracking Control ◽  
Discrete Time Systems ◽  
Time Systems
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