Optimization-based adaptive trajectory tracking controller design of self-balanced vehicle with asymptotic prescribed performance

To handle with the nonlinear external disturbances and unmodeled dynamics of self-balanced vehicle (SBV), a novel adaptive trajectory tracking controller based on asymptotic prescribed performance is proposed. First, a velocity planner based on kinematic is constructed to control the velocity signal to improve the motion stability of SBV. Second, the prescribed performance function (PPF) is designed to prescribe transient-state and steady-state performances (TSP). Afterwards, an optimization-based predictive control (OPC) is proposed for accurate trajectory tracking of SBV. Furthermore, a modified radial basis function neural network (RBFNN) approximator is developed to compensate the unmodeled dynamics and the nonlinear external disturbances of the SBV. The overall system stability is proved with the help of Lyapunov theorem. Finally, the tracking performance and anti-interference robustness of the proposed control method are verified by comparative numerical simulations.