Abstract
This paper presents a model predictive control approach to discrete-time linear parameter varying systems based on a recurrent neural network. The model predictive control problem is formulated as a sequential convex optimization, and it is solved by using a recurrent neural network in real time. The essence of the proposed approach lies in its real-time computational capability with extended applicability. Simulation results are provided to substantiate the effectiveness of the proposed model predictive control approach.
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Yan, Z., Le, X., Wang, J. (2014). Model Predictive Control of Linear Parameter Varying Systems Based on a Recurrent Neural Network. In: Dediu, AH., Lozano, M., Martín-Vide, C. (eds) Theory and Practice of Natural Computing. TPNC 2014. Lecture Notes in Computer Science, vol 8890. Springer, Cham. https://doi.org/10.1007/978-3-319-13749-0_22
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DOI: https://doi.org/10.1007/978-3-319-13749-0_22
Publisher Name: Springer, Cham
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