Inverse Reinforcement Learning for Sojourn-Probability-Based Fuzzy-Switched Systems

This paper investigates the inverse reinforcement learning (IRL) problem for fuzzy switched systems characterized by sojourn probabilities. Classical fuzzy Markov jump system approaches rely on transition probabilities, which are often difficult to estimate, whereas sojourn probabilities provide a more realistic and tractable description of switching behavior. To address the challenges of nonlinear dynamics, uncertain model residence, and the lack of known cost functions, a novel IRL framework is proposed. Different from existing methods, the framework (i) establishes IRL under sojourn-probability-based fuzzy switched systems, (ii) incorporates a dynamic learning rate mechanism that adapts to switching conditions, mitigating oscillations and accelerating convergence, and (iii) introduces constraint-based parameter updates together with a new penalty-matrix update strategy to ensure convergence and reduce computational complexity. Rigorous theoretical analysis demonstrates the stability and convergence of the proposed algorithms. Simulation study on a benchmark tunnel diode circuit confirm that the learner system can accurately reconstruct equivalent cost functions, reproduce expert trajectories, and achieve superior performance compared with conventional IRL methods.