Constrained Fractional-Order Model Predictive Control for Robust Path Following of FWID-AGVs With Asymptotic Prescribed Performance

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This paper proposes a prescribed performance function (PPF)-based constrained fractional-order model predictive controller (CFMPC) for the robust path following of the four-wheel-independent-drive autonomous ground vehicle (FWID-AGV) subject to the model mismatch and external disturbances. First, a PPF-based model predictive controller (MPC) is developed to guarantee the transient-state performance by dealing with the model mismatch. Second, a contraction constraint of the MPC is constructed with an adaptive neural network backstepping method, aiming to ensure the optimal control inputs fall into the region of attraction. Third, a fractional-order cost function of the MPC is discretized to reject the external disturbances and improve the robustness of the controller. Besides, a sufficient condition is given to prove the recursive feasibility of the PPF-based CFMPC, and a Lyapunov function candidate is derived for the asymptotic stability of the closed-loop path following system. The effectiveness and practicability of the proposed method are validated by hardware-in-the-loop tests, respectively.