Direct Adaptive Fuzzy-Wavelet-Neural-Network Control for Electric Two-Wheeled Robotic Vehicles

Abstract

This paper presents a direct adaptive motion controller using fuzzy wavelet neural networks (FWNN) for speed control of an electric two-wheeled robotic vehicle (ETWRV) with unknown parameters and uncertainties. With the decomposition of the overall system into two subsystems: yaw motion control and mobile inverted pendulum, two direct adaptive FWNN motion controllers are respectively proposed to achieve station keeping, speed following and yaw motion control. Asymptotic stabilities of the two controllers with their FWNN weighting updating rules are derived via the Lyapunov stability theory. Simulation results indicate that the proposed controllers are capable of providing satisfactory control actions to steer the vehicle.