A Hybrid Reinforcement Learning and Adaptive Fuzzy Sliding-Mode Control Strategy for Switching Systems
This article investigates the challenging problem of simultaneous control of discrete and continuous inputs in switching systems. Switching systems, which are a special type of hybrid systems, require advanced control methods that can simultaneously manage continuous and discrete inputs due to their hybrid nature. An innovative approach is introduced in this paper, combining Adaptive Fuzzy Sliding Mode Control (AFSMC) with Reinforcement Learning (RL). This method not only manages both types of inputs simultaneously but also operates adaptively and robustly with minimal model information, performing learn and optimize online. To evaluate the performance and verify the proposed algorithm, the two-tank system is selected as a benchmark example in this field. The simulation results showed that the tank level tracking error is reduced to less than 1 cm despite the noise in the measurement with a standard deviation of 0.005 and also the sudden change of the system parameter. Additionally, the number of valve position changes decreased to 6 after 1000 episodes, indicating a significant reduction in switching frequency and an improvement in system stability. This algorithm achieves desired objectives with lower control costs compared to non-hybrid methods (management of discrete and continuous inputs). Furthermore, this approach can serve as a scalable framework for controlling other complex systems with combined inputs across various engineering domains.
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