Journal Article10.1016/J.INS.2019.12.039
Simplified optimized control using reinforcement learning algorithm for a class of stochastic nonlinear systems
65
TL;DR: Simulation demonstrates that the optimized stochastic approach can achieve the desired control objective and can remove the assumption of persistence excitation, which is required for most RL based adaptive optimal control.
read more
About: This article is published in Information Sciences. The article was published on 01 May 2020. The article focuses on the topics: Reinforcement learning & Optimal control.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints
TL;DR: In this article, an adaptive neural network (NN) output feedback optimized control design for a class of strict-feedback nonlinear systems that contain unknown internal dynamics and the states that are immeasurable and constrained within some predefined compact sets is proposed.
649
Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints
TL;DR: In this article , an adaptive neural network (NN) output feedback optimized control design for a class of strict-feedback nonlinear systems that contain unknown internal dynamics and the states that are immeasurable and constrained within some predefined compact sets is proposed.
217
Observer-Based Adaptive Optimized Control for Stochastic Nonlinear Systems With Input and State Constraints.
TL;DR: In this article, an adaptive neural network (NN) optimized output-feedback control problem is studied for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints.
162
Fuzzy Adaptive Optimal Consensus Fault-Tolerant Control for Stochastic Nonlinear Multi-Agent Systems
Kewen Li,Yongming Li +1 more
TL;DR: A fuzzy adaptive distributed optimal consensus fault-tolerant control method is proposed, which can ensure that all signals of the controlled system are semi-globally uniformly ultimately bounded (SGUUB) in probability, and outputs of the follower agents keep consensus with the output of leader.
99
Adaptive Optimized Backstepping Control-Based RL Algorithm for Stochastic Nonlinear Systems With State Constraints and Its Application
01 Oct 2022
TL;DR: In this article , the adaptive neural-network (NN) tracking optimal control problem for stochastic nonlinear systems, which contain state constraints and uncertain dynamics, was investigated, and the novel barrier optimal performance index functions for subsystems were developed.
94
References
•Book
Reinforcement Learning: An Introduction
Richard S. Sutton,Andrew G. Barto +1 more
- 01 Jan 1988
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Fuzzy systems as universal approximators
TL;DR: An additive fuzzy system can uniformly approximate any real continuous function on a compact domain to any degree of accuracy.
1.5K
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.
1.3K
Online actor critic algorithm to solve the continuous-time infinite horizon optimal control problem
Kyriakos G. Vamvoudakis,Frank L. Lewis +1 more
- 14 Jun 2009
TL;DR: This paper presents an online adaptive algorithm implemented as an actor/critic structure which involves simultaneous continuous-time adaptation of both actor and critic neural networks, and calls this ‘synchronous’ policy iteration.
1K
Adaptive neural control of uncertain MIMO nonlinear systems
Shuzhi Sam Ge,Cong Wang +1 more
TL;DR: Adapt neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms that avoid the controller singularity problem completely without using projection algorithms.
945