Linear Parameter Varying Model Predictive-Based Controller for Trajectory Tracking of High-Fidelity Autonomous Vehicle

The tracking control of an unmanned autonomous vehicle (UA V) is one of the key factors determining the performance and effectiveness of autonomous navigation. A controller based on Linear Parameter Varying - Model Predictive Control (LPV - MPC) is designed to track the desired trajectory of the UAV. The Linear Parameter Varying (LPV) modeling and control strategy allows the representation of the UAV nonlinear complex model in a linear-like manner which can be used in a linear Model Predictive Control (MPC). The main advantage of the LPV -MPC technique is its ability to improve tracking solutions very close to nonlinear MPC but with significantly low computational costs, enabling LPV-MPC to be used for real-time operations. The simulation results of this paper show that the designed LPV-MPC performs well in tracking the desired trajectory through each waypoint. Furthermore, experiments with different numerical integration methods have been investigated, especially Euler's method, and the Runge-Kutta method. The results showed that the Runge-Kutta method performed better with smaller horizon periods, with minimum tracking errors and shorter elapsed time.