Robust Model Predictive Control Using a Discrete-Time Recurrent Neural Network

Robust model predictive control (MPC) has been investigated widely in the literature. However, for industrial applications, current robust MPC methods are too complex to employ. In this paper, a discrete-time recurrent neural network model is presented to solve the minimax optimization problem involved in robust MPC. The neural network has global exponential convergence property and can be easily implemented using simple hardware. A numerical example is provided to illustrate the effectiveness and efficiency of the proposed approach.

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