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

0479178 - ÚTIA 2019 RIV US eng J - Journal Article
Pčolka, M. - Žáčeková, E. - Čelikovský, Sergej - Šebek, M.
Toward a Smart Car: Hybrid Nonlinear Predictive Controller With Adaptive Horizon.
IEEE Transactions on Control Systems Technology. Roč. 26, č. 6 (2018), s. 1970-1981, č. článku 08059760. ISSN 1063-6536. E-ISSN 1558-0865
R&D Projects: GA ČR(CZ) GA17-04682S
Institutional support: RVO:67985556
Keywords : Autonomous vehicles * hybrid systems * nonlinear model predictive control (MPC) * optimization * vehicle control
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 5.371, year: 2018
http://library.utia.cas.cz/separaty/2018/TR/celikovsky-0479178.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0275243