Indirect adaptive fuzzy control for a nonholonomic/underactuated wheeled inverted pendulum vehicle based on a data-driven trajectory planner

In this study, we investigate an error data-based trajectory planner and indirect adaptive fuzzy control for a class of wheeled inverted pendulum vehicle systems. Based on the error dynamics, the closed-loop trajectory planner can generate the desired velocity values. Using the virtual acceleration input for the tilt angle subsystem, composite control for the rotational and longitudinal subsystems can be constructed via indirect adaptive fuzzy and sliding mode control approaches to achieve simultaneous velocity tracking and tilt angle stabilization. We rigorously prove the system stability and convergence of the tracking error signals using the Lyapunov theory and LaSalle's invariance theorem. The results of our numerical simulations demonstrated the efficiency of the proposed control strategies and the implementations of the algorithms.