Fusing Deep Reinforcement Learning and Model Predictive Control for Adaptive, Stochastic, and Robust Nonlinear Motion Control of Autonomous Mobile Robots

Baha Zarrouki, Johannes Betz

Keywords: reinforcement learning, robots learning, model predictive control, robust control, adaptive control, autonomous vehicles

Abstract: We present a unified framework for high-speed autonomous vehicle control integrating four key advancements in Nonlinear Model Predictive Control (NMPC) for high-dimensional, real-time control systems: (1) Reduced Robustified NMPC enabling real-time computation through sensitivity-based uncertainty propagation, (2) Stochastic NMPC with adaptive Uncertainty Propagation Horizon (UPH) to prevent covariance explosion, (3) Deep Reinforcement Learning (DRL)-driven parameter adaptation for dynamic disturbance handling, and (4) Safe Reinforcement Learning (RL) with Pareto-constrained weight optimization. Validated through extensive simulation and real-world testing at speeds up to 37.5 m/s (135 km/h), our framework achieves real-time performance with 140 Hz solving frequencies, ensuring 98.77\% constraint satisfaction and sub-0.1m tracking accuracy. All implementations are available open-source.

Submission Number: 26