Motion control of an autonomous vehicle based on wheeled inverted pendulum using neural-adaptive implicit control

Wheeled inverted pendulum (WIP) models have been widely used in the field of autonomous robotics and intelligent vehicles. A novel transportation system, WIP-car is proposed in this paper, which is composed of a mobile wheeled inverted pendulum system, a driven chair, an acceleration pedal and a deceleration pedal, which are used to drive the chair forward or backward such that the car can be accelerated or decelerated. The neural-adaptive implicit control is designed for dynamic balance and stable tracking of desired trajectories of WIP-car. Neither the dynamics nor the dimension of the regulated system is required to be known, while the relative degree of the regulated output is assumed to be known. Under the assumption that WIP-car is feedback linearizable, adaptive neural network is introduced to cancel the inversion dynamics error. Simulation results demonstrate that the system is able to track reference signals satisfactorily with all closed loop signals uniformly bounded.