Optimal Control of Multi-Rate Systems Using Inverse Reinforcement Learning

The goal of this study is to design an optimal tracking controller for multi-rate systems using inverse reinforcement learning (RL). The multi-rate nature of sampling and control periods in controlled devices complicates the implementation of optimal control methods. In this study, the objective performance functions are assumed to be unknown. Under this condition, we first derive an equivalent discrete-time system using the lifting technique. Then, an online model-free inverse RL algorithm is developed to learn both the optimal control policy and the unknown performance objective function for the tracking control of the lifted system. Finally, theoretical analysis and simulation examples demonstrate the effectiveness and advantages of the proposed algorithms.