Evolutionary Optimization of Fuzzy Reinforcement Learning and Its Application to Time-Varying Tracking Control of Industrial Parallel Robotic Manipulators

Abstract

In this article, we contribute to the development of evolutionary optimization of fuzzy reinforcement learning and its application to time-varying tracking control for industrial parallel robotic manipulators. An improved evolutionary social spider optimization (SSO) paradigm with Cauchy mutation is presented to address the optimal fuzzy reinforcement learning problem. The SSO swarm intelligence incorporated with the fuzzy Q-learning strategy, called SSOFQ, is applied to the time-varying tracking control of parallel robotic Stewart manipulators with six degrees of freedom (DOFs). Having the derived kinematics and dynamics of six-DOF Stewart platforms, the SSOFQ is employed to synthesize an intelligent real-time tracking control scheme. More importantly, a custom experimental Stewart platform is constructed using hardware/software codesign and the system-on-a-programmable chip technique. Finally, the simulations, experimental results, and comparative works are provided to validate the efficacy and superiority of the presented SSOFQ control method.