Direct Adaptive Control Based on Improved RBF Neural Network for Omni-directional Mobile Robot

Abstract

proposed for an omni-directional mobile robot (OMR). The OMR is a multi-input and multi-output (MIMO), unmodeled and uncertain nonlinear system which is difficult to be modeled due to a large number of immeasurable and uncertain variables. To model the system exactly and increase the real-time performance, a novel direct adaptive control approach based on improved RBF-NN is designed to approximate the OMR, which needs no explicit knowledge of the uncertain nonlinear MIMO system. Besides the kinematics, the dynamics of the OMR are considered to perform tasks with heavy load transportations or high speed movements. A stable on-line adaptive law is derived and proved using Lyapunov stability theory. The proposed controller is applied the OMR trajectory tracking and shows excellent robustness and stability. The simulation results demonstrate the feasibility and validity of proposed scheme.