Reinforcement Learning-Based Optimal Nonlinear Control for Magnetic Levitation
Magnetic levitation (Maglev) systems are a testbed for advanced control strategies because of their nonlinearity, open-loop instability, and high dynamics. Accurate position control of the levitated object is a difficult task, especially under parameter uncertainties and disturbances. Traditional control approaches, like proportional-integral-derivative (PID) control, are based on linearization of the system and hence suffer from poor performance in the presence of nonlinearities. Sliding mode control (SMC) is a robust nonlinear control method that provides good disturbance rejection and parameter uncertainty tolerance. However, its performance is sensitive to the choice of controller gains, which is often a time-consuming and suboptimal manual task. To overcome this challenge, previous research has used metaheuristic algorithms, like genetic algorithms (GA), for gain tuning. These approaches enhance tracking accuracy and transient response, but are offline and non-adaptive to dynamic system changes. In this work, a Deep Deterministic Policy Gradient (DDPG) reinforcement learning approach is developed for online gain tuning of SMC in a non-linear Maglev system. The DDPG agent engages in continuous interaction with the environment to learn an optimal policy for gain adaptation in response to system dynamics. Asymptotic stability of the closed-loop system is analyzed using Lyapunov’s stability theorem for fixed gains, and the adaptive gain case is discussed. The proposed DDPG-tuned SMC is shown to have better tracking performance, shorter settling time, and better disturbance rejection and parameter uncertainty tolerance than the GA-tuned SMC through simulation studies.