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- W4386071013 abstract "A distributed output-feedback optimal tracking control (OTC) method based on reinforcement learning (RL) is proposed for state-unmeasured multi-agent systems (MASs) under a directed graph. Firstly, the state observers are designed to estimate the states of MASs using the output signals, and the gains of the observers are not required to satisfy the Hurwitz matrix inequality. Then, a class of value functions based on error derivatives is proposed to solve the unbounded problem of traditional value functions for strict-feedback MASs. By using the value functions, the Hamilton-Jacobi Bellman (HJB) equations and the optimal control inputs are derived. The traditional RL-based backstepping control (OBC) methods are difficult to deal with the optimized consensus problem under a directed digraph, it is solved by using the observer-actor-critic structures and the new value functions in this paper, and online learning is implemented. Furthermore, there is no “dimensional pressure” to approximate the optimal control inputs and value functions using actor-critic neural networks (NNs), and the observed error and the consensus error are shown to be bounded. Finally, the effectiveness and advantages of the algorithm are verified by simulation." @default.
- W4386071013 created "2023-08-23" @default.
- W4386071013 creator A5078435124 @default.
- W4386071013 creator A5089822136 @default.
- W4386071013 date "2023-11-01" @default.
- W4386071013 modified "2023-10-17" @default.
- W4386071013 title "Output-Feedback Optimized Consensus for Directed Graph Multi-agent Systems Based on Reinforcement Learning and Subsystem Error Derivatives" @default.
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- W4386071013 doi "https://doi.org/10.1016/j.ins.2023.119577" @default.
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