Path tracking of autonomous driving robotic vehicle based on improved model predictive control

In this paper, a novel path tracking method is proposed to improve the accuracy and stability of path tracking for the autonomous driving robotic vehicle (ADRV). This method is established based on an improved model predictive control (IMMPC) method with some optimization algorithms integrated. To apply this method, firstly, the steering system is simplified and a vehicle error dynamic model considering road curvature is used as the algorithm prediction model. Then, the Whale Optimization algorithm is combined with the Backpropagation (WOA-BP) neural network algorithm to predict the optimal dual time domain parameters, which can improve the algorithm’s robustness against variations in speed and road adhesion coefficients. Meanwhile, the constraints on sideslip angles and environmental road conditions are considered, and integrate them into the nonlinear cost function to avert vehicle rollovers. Furthermore, an enhanced hierarchical particle swarm optimization algorithm with warm start is employed to replace the traditional quadratic sequence method to mitigate local optimal issues, this nonlinear cost function, so that the nonlinear cost function can be optimized. In the last part, co-simulations and driving robot hardware-in-the-loop (HIL) experiments are constructed to validate the stability and precision of the algorithm. The results show that the improved MPC can smoothly track the test path under environmental constraints with higher accuracy and enhanced control stability.