A Real-Time Affordable Predictive Trajectory Tracking Controller for Differential Drive Robots via Imitation Learning

Abstract

Nonlinear Model Predictive Control (NMPC) achieves high-accuracy trajectory tracking for mobile robots but is often too computationally demanding for resourceconstrained platforms. We present a learning-based predictive tracking controller that imitates an expert NMPC policy to achieve real-time performance on differential-drive robots. Our approach combines (a) state-perturbation augmentation with relative-error states to enrich the expert dataset and (b) a lightweight neural architecture based on 1-D convolutions with residual blocks and a physics-aware loss informed by robot kinematics. Expert state-action pairs are collected in a highfidelity, physics-enabled simulator. Finally, we evaluate the resulting policy in a real-time experiment using a Quanser QBOT differential-drive platform, demonstrating tracking accuracy comparable to NMPC while significantly reducing computation time.