Stable Model Predictive Path Integral Control for Aggressive Autonomous Driving

A common challenge with sampling based Model Predictive Control (MPC) algorithms operating in stochastic environments is ensuring stable behavior under sudden state disturbances. Model Predictive Path Integral (MPPI) control is an MPC algorithm that can optimize control of non-linear systems subject to non-differentiable cost criteria. It iteratively computes optimal control sequences by re-using the sequence optimized at the previous timestep as a warm start for the current iteration, which allows rapid convergence thus making it real time capable. This approach is successful in producing a diverse set of behaviors, the most impressive being its ability to control systems at the limits of handling. However, a strong unexpected state disturbance can make the previous control sequence an unsafe initialization for the new state and can result in undesired behavior. In this work, we address this problem by implementing a path tracker that produces control sequences that are used as the initializers for the current timestep, instead of simply re-using the sequence from the previous timestep. The path tracker iteratively computes control sequences that can guide the system to low-cost regions and feeds them into the MPPI framework as a sampling reference. This enforces the algorithm to sample behaviors normally distributed around controls that guide the state back to low-cost regions, even in cases where the state drastically changes. The additional advantage of our method is that it retains the ability to sample diverse and dynamically feasible controls, thus maintaining its ability for motion at the limits of handling. We experimentally verify this method on the AutoRally autonomous research platform, a one-fifth scale race car for aggressive driving tasks, and compare its performance against the most recently published results of MPPI for autonomous driving.Undergraduat