Comparative study of approximate, proximate, and fast model predictive control with applications to autonomous vehicles

In this paper, we present how model predictive control (MPC) is applied to a lane keeping system and how realtime solution is calculated. In the case of fast systems, real-time solution is very difficult to calculate in the sample time because the system has complex formulation and many constraints. The two proposed methods such as proximate MPC and fast MPC reduce the computation time by finding constrained optimal solution. Optimal solution of the proximate MPC is calculated by interpolation parameter regarding pre-computed optimal solutions. And the fast MPC decreases the number of constraints using weighted forgetting factors in order to find effective active constraints. The comparison of performance of the proposed control methods are validated via simulations implemented in real-time environment.