Optimal Tracking Control of Two-time-scale Systems Based on Reinforcement Learning
Aiming at the optimal tracking control problem of two-time-scale systems,a method based on singular perturbation theory and reinforcement learning technique was proposed.Firstly,the system was decomposed into fast and slow subsystems based on singular perturbation theory,solving the singular perturbation parameter problem existing in the system.Secondly,the tracking problem of the two-time-scale system was decomposed into the linear quadratic tracking(LQT)problem for the slow sub-system and the linear quadratic regulator(LQR)problem for the fast subsystem.Furthermore,the policy Q-learning was used to design the controller solving algorithms for the two subsystems respectively.Finally,the results show that the proposed method can achieve the optimal tracking performance of the system.
two-time-scale systemsingular perturbationQ-learningoptimal tracking control
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