Implementation of MPC-Based Path Tracking for Autonomous Vehicles Considering Three Vehicle Dynamics Models with Differe
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Implementation of MPC‑Based Path Tracking for Autonomous Vehicles Considering Three Vehicle Dynamics Models with Different Fidelities Shuping Chen1 · Huiyan Chen1 · Dan Negrut2 Received: 20 November 2019 / Accepted: 2 September 2020 © China Society of Automotive Engineers (China SAE) 2020
Abstract Model predictive control (MPC) algorithm is established based on a mathematical model of a plant to forecast the system behavior and optimize the current control move, thus producing the best future performance. Hence, models are core to every form of MPC. An MPC-based controller for path tracking is implemented using a lower-fidelity vehicle model to control a higher-fidelity vehicle model. The vehicle models include a bicycle model, an 8-DOF model, and a 14-DOF model, and the reference paths include a straight line and a circle. In the MPC-based controller, the model is linearized and discretized for state prediction; the tracking is conducted to obtain the heading angle and the lateral position of the vehicle center of mass in inertial coordinates. The output responses are discussed and compared between the developed vehicle dynamics models and the CarSim model with three different steering input signals. The simulation results exhibit good path-tracking performance of the proposed MPC-based controller for different complexity vehicle models, and the controller with high-fidelity model performs better than that with low-fidelity model during trajectory tracking. Keywords Autonomous vehicles · Model predictive control · Vehicle dynamics · Path tracking Abbreviations C.M. Center of mass DOF Degree of freedom LMPC Linear model predictive control MPC Model predictive control
1 Introduction Path-tracking control refers to the ability of autonomous vehicles to follow a given path by controlling vehicle speed and steering. An ideal control algorithm can maintain the vehicle along the desired path using its current measurements while enforcing specific limits. The tracking errors, which mainly refers to a vehicle’s deviations in distance and heading from the desired path, are compensated by steering and speed signals continually generated from the tracking controller. Various controllers such as PID control [1], * Shuping Chen [email protected] 1
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
2
optimal control [2], and the backstepping and sliding mode control [3] have been developed for this purpose using classical control theory, modern control theory, robust control theory, and similar approaches. However, these control methods ignore the actuator saturation limit or physical constraints of the vehicle in the process of path tracking. The model predictive control (MPC) method combines a prediction model, a receding horizon optimization, and a feedback correction. It is advantageous in solving a control problem with multiple constraints. Therefore, this method is used to b
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