Abstract
This paper presents a novel model predictive control (MPC) approach to tracking control of mobile robots based on recurrent neural networks (RNNs). The tracking control problem is firstly formulated as a sequential dynamic optimization problem in framework of MPC. Then a novel neurodynamic approach is developed for computing the optimal control signals in real time, where multiple RNNs are applied in a collective fashion. The proposed approach enables MPC of mobile robots to be synthesized in real time. Simulation results are provided to substantiate the effectiveness of the proposed approach.
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Bi, S., Zhang, G., Xue, X., Yan, Z. (2015). Real-Time Robust Model Predictive Control of Mobile Robots Based on Recurrent Neural Networks. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_33
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DOI: https://doi.org/10.1007/978-3-319-26555-1_33
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