Reinforcement Learning-Based Cooperative Optimal Output Regulation Via Distributed Adaptive Internal Model
In this article, a data-driven distributed control method is proposed to solve the cooperative optimal output regulation problem of leader-follower multiagent systems. Different from traditional studies on cooperative output regulation, a distributed adaptive internal model is originally developed, which includes a distributed internal model and a distributed observer to estimate the leader's dynamics. Without relying on the dynamics of multiagent systems, we have proposed two reinforcement learning algorithms, policy iteration and value iteration, to learn the optimal controller through online input and state data, and estimated values of the leader's state. By combining these methods, we have established a basis for connecting data-distributed control methods with adaptive dynamic programming approaches in general since these are the theoretical foundation from which they are built.
W. Gao et al., "Reinforcement Learning-Based Cooperative Optimal Output Regulation Via Distributed Adaptive Internal Model," IEEE Transactions on Neural Networks and Learning Systems, Institute of Electrical and Electronics Engineers (IEEE), Jan 2021.
The definitive version is available at https://doi.org/10.1109/TNNLS.2021.3069728
Electrical and Computer Engineering
Keywords and Phrases
Adaptation Models; Adaptive Optimal Control; Cooperative Output Regulation; Distributed Adaptive Internal Model; Multi-Agent Systems; Optimal Control; Power System Dynamics; Regulation; Reinforcement Learning.; Symmetric Matrices; Vehicle Dynamics
International Standard Serial Number (ISSN)
Article - Journal
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01 Jan 2021