Model predictive control with fuzzy logic switching for path tracking of autonomous vehicles.

This paper introduces an integrated path tracking control strategy for autonomous vehicles. The proposed control strategy is based on a multi-input multi-output linear model predictive control (LMPC) with a fuzzy logic switching system. The designed MPC is based on Laguerre networks. The main target of the designed MPC is to produce the optimal control signals of the steering angle and the angular velocity while considering the physical constraints of the control signals and the measurements noise. Since the vehicle model is highly nonlinear and is operated over a wide range of operating points, different linearized models are obtained. The controller parameters for each linear model are designed and tuned. The gab metric analysis is used to select a number of these models to simplify the design of the proposed controller. Then, these models are combined using a fuzzy logic controller to switch between them. To test the proposed controller performance, different paths are generated using path planning algorithms. These paths simulate different vehicle maneuvers scenarios. The simulation results show that the designed tracking controller has a tracking performance on different designed paths better than that of a Linear quadratic gaussian (LQG) tracking controller, discussed in this paper. • MIMO model predictive control is designed based on Laguerre filter for autonomous vehicles. • Fuzzy system is used to smoothly switch between linearized models. • Gap-metric analysis is used to reduce the number of the linearized models. • Proposed multi-model controller perfectly tracks paths regardless their complexity. [ABSTRACT FROM AUTHOR]