Constrained Deep Learning Based Nonlinear Model Predictive Control

Abstract

Learning-based model predictive control (MPC) is an approach designed to reduce the computational cost of MPC. In this paper, a constrained deep neural network (DNN) design is proposed to learn MPC policy for nonlinear systems. Using constrained training of neural networks, MPC constraints are enforced effectively. Furthermore, recursive feasibility and robust stability conditions are derived for the learning-based MPC approach. Additionally, probabilistic feasibility and optimality empirical guarantees are provided for the learned control policy. The proposed algorithm is implemented on the Furuta pendulum and control performance is demonstrated and compared with the exact MPC and the normally trained learning-based MPC. The results show superior control performance and constraint satisfaction of the proposed approach.

arXiv commentDue to unsolvable problems in the proof of robustness and stability, the paper is withdrawn from arXiv. Please do not cite it