Journal ArticleDOI
Simultaneous identification and optimal tracking control of unknown continuous-time systems with actuator constraints
Amardeep Mishra,Satadal Ghosh +1 more
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In order to obviate the requirement of drift dynamics in adaptive dynamic programming, integral reinforcement learning (IRL) has been proposed as an alternate formulation of Bellman equation.Abstract:
In order to obviate the requirement of drift dynamics in adaptive dynamic programming, integral reinforcement learning (IRL) has been proposed as an alternate formulation of Bellman equation. Howev...read more
Citations
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Journal ArticleDOI
Off-Policy Model-Free Learning for Multi-Player Non-Zero-Sum Games With Constrained Inputs
TL;DR: In this paper , a model-free iterative method is obtained by using the off-policy integral reinforcement learning (IRL) scheme based on the model-based policy iteration (PI) method.
Journal ArticleDOI
Adaptive optimal trajectory tracking control of AUVs based on reinforcement learning.
Zhifu Li,Ming Shyan Wang,Ge Ma +2 more
TL;DR: In this article , an adaptive model-free optimal reinforcement learning (RL) neural network (NN) control scheme based on filter error is proposed for the trajectory tracking control problem of an autonomous underwater vehicle (AUV) with input saturation.
Journal ArticleDOI
A brief survey on nonlinear control using adaptive dynamic programming under engineering-oriented complexities
TL;DR: Adaptive dynamic programming (ADP) can be used in finding the solution for the control problem in the presence of known/unknown nonlinear dynamics as discussed by the authors , which is appropriate to be used to find the solution of the control problems.
Journal ArticleDOI
Off-Policy Model-Free Learning for Multi-Player Non-Zero-Sum Games With Constrained Inputs
TL;DR: In this article , a model-free iterative method is obtained by using the off-policy integral reinforcement learning (IRL) scheme based on the model-based policy iteration (PI) method.
Proceedings ArticleDOI
Continual Optimal Adaptive Tracking of Uncertain Nonlinear Continuous-time Systems using Multilayer Neural Networks
TL;DR: In this paper , a lifelong integral reinforcement learning (LIRL)-based optimal tracking scheme for uncertain nonlinear continuous-time (CT) systems using multilayer neural network (MNN) was provided.
References
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Journal ArticleDOI
Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach
Murad Abu-Khalaf,Frank L. Lewis +1 more
TL;DR: It is shown that the constrained optimal control law has the largest region of asymptotic stability (RAS) and the result is a nearly optimal constrained state feedback controller that has been tuned a priori off-line.
Journal ArticleDOI
Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem
TL;DR: An online algorithm based on policy iteration for learning the continuous-time optimal control solution with infinite horizon cost for nonlinear systems with known dynamics, which finds in real-time suitable approximations of both the optimal cost and the optimal control policy, while also guaranteeing closed-loop stability.
Journal ArticleDOI
Data-Driven Robust Approximate Optimal Tracking Control for Unknown General Nonlinear Systems Using Adaptive Dynamic Programming Method
TL;DR: A novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method and a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method.
Journal ArticleDOI
A novel actor-critic-identifier architecture for approximate optimal control of uncertain nonlinear systems
Shubhendu Bhasin,Rushikesh Kamalapurkar,Marcus Johnson,Kyriakos G. Vamvoudakis,Frank L. Lewis,Warren E. Dixon +5 more
TL;DR: An online adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem for continuous-time uncertain nonlinear systems using a novel actor-critic-identifier (ACI) architecture to approximate the Hamilton-Jacobi-Bellman equation.
Journal ArticleDOI
Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning
Hamidreza Modares,Frank L. Lewis +1 more
TL;DR: This formulation extends the integral reinforcement learning (IRL) technique, a method for solving optimal regulation problems, to learn the solution to the OTCP, and it also takes into account the input constraints a priori.
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