Reinforcement Learning and Disturbance Observer Based Optimal Control for Uncertain Systems
2023 China Automation Congress (CAC)(2023)
Abstract
This paper investigates the robust adaptive optimal control problem for linear systems in the presence of matching uncertainties. A nominal controller utilizing Q-Iearning algorithm is designed for the nominal linear system without uncertainties. Introducing a disturbance observer serves the purpose of actively estimating uncertainties, enabling proactive compensation for matching uncertainties. The analysis of the closed-loop system's stability under the robust adaptive optimal controller is conducted, presenting sufficient conditions to ensure its stability. Finally, the control algorithm is validated using a two-wheeled mobile robot as the experimental platform.
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Key words
Disturbance observer,Reinforcement learning,Robust optimal control
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