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.
CITATION STYLE
Yan, Z., Le, X., & Wang, J. (2014). Model predictive control of linear parameter varying systems based on a recurrent neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8890, pp. 255–266). Springer Verlag. https://doi.org/10.1007/978-3-319-13749-0_22
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