Toward a Smart Car: Hybrid Nonlinear Predictive Controller With Adaptive Horizon

This paper focuses on the development of an optimization algorithm for car motion predictive control that addresses both hybrid car dynamics and hybrid minimization criterion. Instead of solving computationally demanding nonlinear mixed-integer programming task or approximating the hybrid dynamics/criterion, the Hamiltonian-switching hybrid nonlinear predictive control algorithm developed in this paper incorporates the information about hybridity directly into the optimization routine. To decrease the time complexity, several adaptive prediction horizon approaches are proposed, and for some of them, it is shown that they preserve maneuverability-related properties of the car. All developed alternatives are verified on an example of a motion control of a racing car and compared with the approximation-based nonlinear predictive control and a commercial product. Moreover, a sensitivity analysis examining robustness of the algorithm is included as well.

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