Path-Following Guidance Using Model Predictive Path Integral Control

Abstract

Model Predictive Path Integral (MPPI) control framework algorithms have been studied for use in autonomous control systems because they are convenient to implement using model predictive trajectory samples with a stochastic control approach. They can also deal extensivlely with complex desired costs and constraints. This paper presents a path following a control algorithm based on the model predictive path integral control framework for autonomous vehicles. By using the importance sampling method in the model predictive control, the iterative path integral provides acceleration commands for a vehicle, allowing it to track a virtual target on a desired path and achieve the optimal trajectory under the constraints. The optimal acceleration commands are updated using a stochastic control approach using model predictive trajectory samples. This approach allowed us to efficiently solve the nonlinear control problem with complex costs and constraints, without intractable convexification or linearization. We implemented the Graphics Processing Unit (GPU) algorithm to show that this algorithm can quickly compute this problem. We tested the algorithm on various paths and under wind disturbance, using a nonlinear disturbance observer that allowed us to predict the model more correctly in an uncertain environment. The simulation results show that the algorithm is effective and applicable to path-following guidance for various paths under disturbances.