Model Predictive Control with Laguerre Function based on Social Ski Driver Algorithm for Autonomous Vehicle

Abstract

The steering control of the autonomous vehicles represents an avital issue in the vehicular system. The model predictive control was proved as an effective controller. However, the representation of the model predictive control (MPC) by a large prediction horizon and control horizon requires a large number of parameters and it is complicated. Discrete-time Laguerre functions can cope with this issue to represent the MPC with few parameters. Whilst, the Laguerre functions require a proper tuning for its parameters in order to provide a good response with MPC. This paper introduces a new design method to tune the parameters of the MPC with the Laguerre function by a new artificial intelligence (AI) technique named social ski driver algorithm (SSDA). The proposed MPC based on the SSDA is applied to adjust the steering angle of an autonomous vehicle including vision dynamics. Further test scenarios are created to ensure the effectiveness of the proposed control to cope with the variations of road curvatures.