Data-Driven Dual-Channel Adaptive MPC for Mobile Robot Trajectory Tracking

To address adaptability limitations in conventional model predictive control, this paper proposes a data-driven dual-channel (D-Channel) adaptive framework for wheeled mobile robots. By extending the traditional single-channel architecture into a parallel dual-network configuration and integrating an offline reinforcement learning channel, the proposed method improves control precision and robustness. The D-Channel structure refines predictive outputs, while the reinforcement learning module enhances adaptability to dynamic disturbances. Comprehensive simulations and hardware-in-the-loop experiments show that the D-Channel radial basis function neural networks-model predictive control outperforms single-channel counterparts. The results demonstrate improved tracking accuracy, faster convergence, and reduced steady-state error, confirming the effectiveness of combining data-driven learning with predictive optimization for complex control tasks.