Abstract
This paper presents a linearization method for the vehicle and tire models under the model predictive control (MPC) scheme, and proposes a linear model-based MPC path-tracking steering controller for autonomous vehicles. The steering controller is designed to minimize lateral path-tracking deviation at high speeds. The vehicle model is linearized by a sequence of supposed steering angles, which are obtained by assuming the vehicle can reach the desired path at the end of the MPC prediction horizon and stay in a steady-state condition. The lateral force of the front tire is directly used as the control input of the model, and the rear tire’s lateral force is linearized by an equivalent cornering stiffness. The course-direction deviation, which is the angle between the velocity vector and the path heading, is chosen as a control reference state. The linearization model is validated through the simulation, and the results show high prediction accuracy even in regions of large steering angle. This steering controller is tested through simulations on the CarSim-Simulink platform (R2013b, MathWorks, Natick, MA, USA), showing the improved performance of the present controller at high speeds.
Highlights
Autonomous vehicle technology aims to increase driving safety, reduce traffic congestion and emissions, and improve energy efficiency [1,2]
Erlien et al [7] introduced an affine approximation linearization method to handle the nonlinearity of the tires in the model predictive control (MPC) scheme, this approach is inaccurate when the length of the prediction horizon is larger
In order to simplify the nonlinear relationship between the actuation δ and the vehicle dynamic states while taking the saturation of the tire into account, the front lateral force Fyf is considered as the control input of the model
Summary
Autonomous vehicle technology aims to increase driving safety, reduce traffic congestion and emissions, and improve energy efficiency [1,2]. Kritayakirana et al [6] used the center of percussion as a reference point for calculating the steering command, in order to minimize both the heading and lateral deviations Their algorithm has lower complexity and can maintain stability even when the rear tires are saturated. Tagne et al [14] presented an adaptive controller and used the steady-state sideslip and yaw rate to help bring the operating point to the desired equilibrium quickly Their controller is not capable of accurate path tracking in the tire-friction limit. Raffo et al [18] presented an MPC path-tracking controller with a linear kinematic model to achieve the desired performance during high-speed driving. A linear-model MPC path-tracking steering controller, using the direction deviation between the vehicle’s velocity vector and the path heading as the control reference state, is designed.
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