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Learning-Based Risk-Averse Model Predictive Control for Adaptive Cruise Control with Stochastic Driver Models⁎This work was supported by the Ford-KU Leuven Research Alliance. The work of P. Patrinos was supported by: FWO projects: No. G086318N; No. G086518N; Fonds de la Recherche Scientifique–FNRS, the Fonds Wetenschappelijk Onderzoek–Vlaanderen under EOS Project No. 30468160 (SeLMA), Research Council KU Leuven C1 project No. C14/18/068. Issue 2 (2020)
Record Type:
Journal Article
Title:
Learning-Based Risk-Averse Model Predictive Control for Adaptive Cruise Control with Stochastic Driver Models⁎This work was supported by the Ford-KU Leuven Research Alliance. The work of P. Patrinos was supported by: FWO projects: No. G086318N; No. G086518N; Fonds de la Recherche Scientifique–FNRS, the Fonds Wetenschappelijk Onderzoek–Vlaanderen under EOS Project No. 30468160 (SeLMA), Research Council KU Leuven C1 project No. C14/18/068. Issue 2 (2020)
Main Title:
Learning-Based Risk-Averse Model Predictive Control for Adaptive Cruise Control with Stochastic Driver Models⁎This work was supported by the Ford-KU Leuven Research Alliance. The work of P. Patrinos was supported by: FWO projects: No. G086318N; No. G086518N; Fonds de la Recherche Scientifique–FNRS, the Fonds Wetenschappelijk Onderzoek–Vlaanderen under EOS Project No. 30468160 (SeLMA), Research Council KU Leuven C1 project No. C14/18/068.
Abstract: We propose a learning-based, distributionally robust model predictive control approach towards the design of adaptive cruise control (ACC) systems. We model the preceding vehicle as an autonomous stochastic system, using a hybrid model with continuous dynamics and discrete, Markovian inputs. We estimate the (unknown) transition probabilities of this model empirically using observed mode transitions and simultaneously determine sets of probability vectors (ambiguity sets) around these estimates, that contain the true transition probabilities with high confidence. We then solve a risk-averse optimal control problem that assumes the worst-case distributions in these sets. We furthermore derive a robust terminal constraint set and use it to establish recursive feasibility of the resulting MPC scheme. We validate the theoretical results and demonstrate desirable properties of the scheme through closed-loop simulations.