Electrical Vehicle Path Tracking Based Model Predictive Control With a Laguerre Function and Exponential Weight

Abstract

Model predictive control (MPC) is advantageous for designing an electrical vehicle path-tracking controller, but the high computational complexity, mathematical problem, and parameterization challenge adversely affect the control performance. Hence, based on a fully actuated-by-wire electrical vehicle (FAW-EV), a novel path-tracking controller based on improved MPC with a Laguerre function and exponential weight (LEMPC) is designed. The massive optimization control parameters of MPC with a long control horizon are reduced by introducing a fitting orthogonal sequence consisting of Laguerre functions, thereby substantially reducing the computational complexity without sacrificing the tracking accuracy. An exponential weight with decreasing characteristic is introduced to MPC to solve the mathematical problem, thereby improving the robustness of the path tracking controller. In addition, the parameterization access for online adjusting path tracking control performance can be provided by the proposed method. The path tracking motion realization for FAW-EV is subsequently illustrated. Finally, several simulations are implemented to verify the advantages of the proposed method.

Keywords

• Path track
• electrical vehicle
• model predictive control (MPC)
• Laguerre function
• exponential weight