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

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.

Initiation and Stabilization of Drifting Motion of a Self-driving Vehicle with a Reinforcement Learning Agent

“…As for previous examples and results for the implementation of self-drifting, using linear control methods seem to work successfully for steady-state drift problems in simulation [4] [5] and also in real-life applications [6]. The simulation application of the MPC (Model Predictive Control) controller for drift stabilization tasks has also been successful so far [7]. In each of these cases, a car with a high rear traction force considered to be essential for achieving satisfying performance, based on real-life observations and measurements data.…”

Sim-to-Real Application of Reinforcement Learning Agents for Autonomous, Real Vehicle Drifting

“…MPC (Model Predictive Control) has proven successful for both stabilization and track-following drift tasks [18,19]. It also performed well when simulating changing road conditions, thus indicating a good adaptive property.…”

LTV-MPC Approach for Automated Vehicle Path Following at the Limit of Handling

“…The advantages of automatization of different vehicle functions include the potential to improve road safety, reduce pollutant emissions and traveling times, and eliminate human errors, which are the primary cause of accidents. An automated vehicle can avoid collisions by using the steering and the braking system conventionally; furthermore, a new alternative solution for accident prevention is to force the vehicle into an unstable state, in which a control software can drive the vehicle at a level as high as a professional driver, for example, in case of a drift maneuver [ 1 , 2 , 3 ]. Each of these studies aims to drive a vehicle at the level of a professional human driver.…”

“…Each of these studies aims to drive a vehicle at the level of a professional human driver. In [ 1 ], an LQ controller is used for steady-state drifting, while in [ 2 ], an MPC is applied for drifting in varying road surface conditions, and in [ 3 ], the drift is realized by a reinforcement learning algorithm.…”