Fusion of Metaheuristic Fuzzy Neural Network and Self-tuning Autonomous Control for Omnidirectional Mobile Platforms in Robotic Cyber-Physical Systems

This paper contributes to the fusion of metaheuristic fuzzy neural network (FNN) and self-tuning autonomous control for omnidirectional mobile platforms in robotic cyber-physical systems (RCPSs). A cyber grey wolf optimization (GWO)-based FNN computing is incorporated with the backstepping control scheme and dynamic modeling to achieve autonomous control for the omnidirectional Mecanum platforms with uncertainties for RCPSs, called GWOFNN. The proposed cyber GWOFNN computing method is employed to address the self-tuning autonomous control problem of RCPS omnidirectional platforms by considering modeling uncertainties and unknown frictions. Numerical simulations and real-time experiments via field-programmable gate array (FPGA) realization are provided to illustrate the efficacy, applicability and merits of the presented RCPS GWOFNN real-time self-tuning cyber control strategy. Through comparison works, the advantages of the proposed GWOFNN computing are validated to accomplish autonomous control for Mecanum mobile RCPSs in polar space.