Linear model predictive control for lane keeping and obstacle avoidance on low curvature roads

Abstract

Abstract-This paper presents a control architecture based on a linear MPC formulation that addresses the lane keeping and obstacle avoidance problems for a passenger car driving on low curvature roads. The proposed control design decouples the longitudinal and lateral dynamics in two successive stages. First, plausible braking or throttle profiles are defined over the prediction horizon. Then, based on these profiles, linear timevarying models of the vehicle lateral dynamics are derived and used to formulate the associated linear MPC problems. The solutions of the optimization problems are used to determine for every time step, the optimal braking or throttle command and the corresponding steering angle command. Simulations show the ability of the controller to overcome multiple obstacles and keep the lane. Experimental results on an autonomous passenger vehicle driving on slippery roads show the effectiveness of the approach.

Comparison of trajectory tracking controllers for autonomous vehicles

“…Control methods include sliding mode control [1], [2], [3], flatness-based control [4], [5], optimal linear-quadratic control [6], backstepping-based strategies [7], [8], [9], optimal preview control [10] and optimization-based methods like model predictive control (MPC) [11], [12].…”

Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck

“…In [47], the discrete time linear system is based on successive online linearizations of the nonlinear vehicle model, and the stability conditions for LTV-MPC controllers are also presented. Following the same reasoning, a control architecture based on LTV-MPC is presented in [49] to address the lane keeping and obstacle avoidance problems for a passenger car driving on low curvature and low friction roads. A study about the use of kinematic and dynamic vehicle models for MPC control design is detailed in [50].…”

Integrated stability and traction control for electric vehicles using model predictive control

“…The controller is tested in simulations with different cars and provides satisfactory performance. A similar method is used in (Falcone, Tufo, Borrelli, Asgari & Tseng, 2007;Turri, Carvalho, Tseng, Johansson & Borrelli, 2013;Beal, 2011;Jalaliyazdi, Khajepour, Chen & Litkouhi, 2015). To improve the robustness of the controller to uncertainties such as modeling uncertainties, some authors use reachability analysis and invariant set theory (Blanchini and Miani, 2008;Raković and Barić, Morari).…”