Soft constraints in the robust MPC design via LMIs

Oravec, Juraj; Bakošová, Monika

doi:10.1109/acc.2016.7525470

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2016 American Control Conference (ACC) 2016

DOI: 10.1109/acc.2016.7525470

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Soft constraints in the robust MPC design via LMIs

Juraj Oravec

Monika Bakošová

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2023

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Offline Computation of the Explicit Robust Model Predictive Control Law Based on Deep Neural Networks

Jiang²,

et al. 2023

Symmetry

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A significant challenge in robust model predictive control (MPC) is the online computational complexity. This paper proposes a learning-based approach to accelerate online calculations by combining recent advances in deep learning with robust MPC. The use of soft constraint variables addresses feasibility issues in the robust MPC design, while the employment of a symmetrical structure deep neural network (DNN) approximates the robust MPC control law. The symmetry of the network structure facilitates the training process. The use of soft constraints expands the feasible region and also increases the complexity of the training data, making the network difficult to train. To overcome this issue, a dataset construction method is employed. The performance of the proposed method is demonstrated through simulated examples, and the proposed algorithm can be applied to control systems in various fields such as aerospace, three-dimensional printing, optical imaging, and chemical production.

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Offline Computation of the Explicit Robust Model Predictive Control Law Based on Deep Neural Networks

Jiang²,

et al. 2023

Symmetry

View full text Add to dashboard Cite

show abstract

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Soft constraints in the robust MPC design via LMIs

Cited by 1 publication

References 18 publications

Offline Computation of the Explicit Robust Model Predictive Control Law Based on Deep Neural Networks

Offline Computation of the Explicit Robust Model Predictive Control Law Based on Deep Neural Networks

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