Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces

Szilárd Czibere, Ádám Domina, Ádám Bárdos and Zsolt Szalay
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Szilárd Czibere: Department of Automotive Technologies, Budapest University of Technology and Economics, 6 Stoczek St., Building J, H-1111 Budapest, Hungary
Ádám Domina: Department of Automotive Technologies, Budapest University of Technology and Economics, 6 Stoczek St., Building J, H-1111 Budapest, Hungary
Ádám Bárdos: Department of Automotive Technologies, Budapest University of Technology and Economics, 6 Stoczek St., Building J, H-1111 Budapest, Hungary
Zsolt Szalay: Department of Automotive Technologies, Budapest University of Technology and Economics, 6 Stoczek St., Building J, H-1111 Budapest, Hungary

Energies, 2021, vol. 14, issue 20, 1-17

Abstract: Electronic vehicle dynamics systems are expected to evolve in the future as more and more automobile manufacturers mark fully automated vehicles as their main path of development. State-of-the-art electronic stability control programs aim to limit the vehicle motion within the stable region of the vehicle dynamics, thereby preventing drifting. On the contrary, in this paper, the authors suggest its use as an optimal cornering technique in emergency situations and on certain road conditions. Achieving the automated initiation and stabilization of vehicle drift motion (also known as powerslide) on varying road surfaces means a high level of controllability over the vehicle. This article proposes a novel approach to realize automated vehicle drifting in multiple operation points on different road surfaces. A three-state nonlinear vehicle and tire model was selected for control-oriented purposes. Model predictive control (MPC) was chosen with an online updating strategy to initiate and maintain the drift even in changing conditions. Parameter identification was conducted on a test vehicle. Equilibrium analysis was a key tool to identify steady-state drift states, and successive linearization was used as an updating strategy. The authors show that the proposed controller is capable of initiating and maintaining steady-state drifting. In the first test scenario, the reaching of a single drifting equilibrium point with −27.5° sideslip angle and 10 m/s longitudinal speed is presented, which resulted in −20° roadwheel angle. In the second demonstration, the setpoints were altered across three different operating points with sideslip angles ranging from −27.5° to −35°. In the third test case, a wet to dry road transition is presented with 0.8 and 0.95 road grip values, respectively.

Keywords: autonomous drifting; model predictive control (MPC); successive linearization; adaptive control; vehicle motion control; varying road surfaces; vehicle dynamics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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