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. INDEX TERMS Path track, electrical vehicle, model predictive control (MPC), Laguerre function, exponential weight.

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

“…To realize vehicle path tracking MPC control under different speed and different curvature conditions, Reference [69][70][71][72] proposed parameters adaptive MPC control strategies using fuzzy rules and multiple controllers combination to achieve adaptive adjustment of control parameters under different operating conditions. Reference [73][74][75][76][77] studied the MPC fast online solution methods of path tracking for autonomous vehicle using differential evolution algorithm, Laguerre function, and look-up table to improve the efficiency of MPC controller calculations. When the vehicle is under high-speed, large curvature and complex operating conditions, the vehicle dynamics show nonlinearity, strong coupling, and parameter uncertainty.…”

A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking

“…The path tracking model is shown in Figure 5, illustrating the relationship between the lateral deviation e, the heading deviation θ e , and the distance s along the path. In most of the existing path-tracking controllers, the lateral deviation e, the heading deviation θ e are chosen as the reference states [28,29,32,33], solving the optimization problem by minimizing e and θ e . However, path tracking lateral deviation is minimized when vehicle sideslip is held tangent to the desired path at all times [19,20,37].…”

“…Ref. [33] studied the control incremental modeling method based on Laguerre function to simplify parameter complexity in prediction horizon. Ref.…”

Path Tracking Control of an Articulated Road Roller With Sideslip Compensation

“…Path tracking is one of the most elemental and important problems in the research of unmanned road roller, whose purpose is to ensure the vehicle can lock into a desired path with a designed control law [4]- [6]. It is important to point out that a road roller is an articulated steering type vehicle, which has two frames (front and rear) articulated by an active revolute joint.…”