Integral reinforcement learning based decentralized optimal tracking control of unknown nonlinear large-scale interconnected systems with constrained-input
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.09.011
Abstract: Abstract This paper deals with the decentralized optimal tracking control problem of large-scale interconnected systems with constrained-input. The large-scale interconnected systems are firstly transformed to several nominal isolated subsystems. Then, nominal isolated subsystems tracking problem… read more here.
Keywords: large scale; constrained input; scale interconnected; control ... See more keywords
Action Mapping: A Reinforcement Learning Method for Constrained-Input Systems.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3138924
Abstract: Existing approaches to constrained-input optimal control problems mainly focus on systems with input saturation, whereas other constraints, such as combined inequality constraints and state-dependent constraints, are seldom discussed. In this article, a reinforcement learning (RL)-based… read more here.
Keywords: constrained input; systems action; reinforcement learning; action mapping ... See more keywords
Optimal Leader–Follower Consensus for Constrained-Input Multiagent Systems With Completely Unknown Dynamics
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2020.3011184
Abstract: In this article, we investigate a novel reinforcement-learning (RL)-based scheme to address the optimal leader–follower consensus issue for constrained-input continuous-time multiagent systems. First, as for input-constrained problems, the fundamental smooth assumption is not satisfied for… read more here.
Keywords: leader follower; multiagent systems; follower consensus; constrained input ... See more keywords