Dynamics estimation and deep reinforcement learning based fractal inspired sliding mode control of Stewart platform

This paper presents a novel nonlinear Stewart control method, which combines real-time dynamics estimation and an intelligent nonlinear control approach. The proposed solution combines (i) a Fractal-inspired Radial Basis Function (FRBF) network to estimate unknown system dynamics and (ii) a Deep Reinforcement Learning based Fractal-inspired Sliding Mode Controller (DRL-FSMC) to track the trajectory in uncertain conditions. The FRBF network estimates unknown disturbances with 43% parameter efficiency compared to standard RBF networks. The DRL-FSMC offers more robustness, less chattering and automatically tunes control gains online using a Soft Actor-Critic (SAC) algorithm, which enables robust trajectory tracking under parametric uncertainty and external disturbances. The reliability and accuracy of the proposed controller is proved by extensive simulations in different motion scenarios, such as step-inputs, sinusoidal paths, and uncertainty scenarios. The proposed controller exhibits better convergence rate, better tracking, and greater resistance to disturbances and modeling errors compared to the traditional PID and classical SMC approaches. The proposed approach, according to these results, has a considerable potential of applications that require agility and stability, including flight simulation and robotic-assisted surgery.