Adaptive robust motion control using fuzzy wavelet neural networks for uncertain electric two-wheeled robotic vehicles

This paper presents an adaptive robust motion control using fuzzy wavelet neural networks (FWNN) for a electric two-wheeled robotic vehicles (ETWRV). A mechatronic system structure driven by two DC motors is briefly described, and its nonlinear mathematical modeling incorporating the friction between the wheels and the motion surface is derived. With the decomposition of the overall system into two subsystems: yaw control and inverted pendulum, two intelligent adaptive FWNN controllers are proposed to achieve self-balancing, speed tracking and yaw motion control. Simulation results indicate that the proposed controllers are capable of providing appropriate control actions to steer the vehicle in desired manners.