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

Wanqi Guo & Shigeyuki Tateno

International Journal of Data Warehousing and Mining2026https://doi.org/10.4018/ijdwm.406757article
ABDC C
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.4018/ijdwm.406757

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@article{wanqi2026,
  title        = {{Data-Driven Dual-Channel Adaptive MPC for Mobile Robot Trajectory Tracking}},
  author       = {Wanqi Guo & Shigeyuki Tateno},
  journal      = {International Journal of Data Warehousing and Mining},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijdwm.406757},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.