Data-driven Acceleration of MPC with Guarantees
This addresses the need for faster MPC in real-time control tasks, representing a novel method for a known bottleneck.
The paper tackles the problem of Model Predictive Control (MPC) being too slow for low-latency applications by introducing a data-driven framework that replaces online optimization with a nonparametric policy from offline solutions, resulting in a policy that is 100 to 1000 times faster than standard MPC with only a modest optimality loss.
Model Predictive Control (MPC) is a powerful framework for optimal control but can be too slow for low-latency applications. We present a data-driven framework to accelerate MPC by replacing online optimization with a nonparametric policy constructed from offline MPC solutions. Our policy is greedy with respect to a constructed upper bound on the optimal cost-to-go, and can be implemented as a nonparametric lookup rule that is orders of magnitude faster than solving MPC online. Our analysis shows that under sufficient coverage condition of the offline data, the policy is recursively feasible and admits provable, bounded optimality gap. These conditions establish an explicit trade-off between the amount of data collected and the tightness of the bounds. Our experiments show that this policy is between 100 and 1000 times faster than standard MPC, with only a modest hit to optimality, showing potential for real-time control tasks.