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

Authors:
Yunpeng Pan;Jun Wang
Affiliations:
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong;Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

Citing 9
Cited 0

Model Predictive Control in the Process Industry

Model Predictive Control in the Process Industry
A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations

Neural Networks
A novel neural network for a class of convex quadratic minimax problems

Neural Computation
Survey Constrained model predictive control: Stability and optimality

Automatica (Journal of IFAC)
Convergence analysis of a discrete-time recurrent neural network to perform quadratic real optimization with bound constraints

IEEE Transactions on Neural Networks
A neural network for a class of convex quadratic minimax problems with constraints

IEEE Transactions on Neural Networks
Solving Pseudomonotone Variational Inequalities and Pseudoconvex Optimization Problems Using the Projection Neural Network

IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application

IEEE Transactions on Neural Networks
A One-Layer Recurrent Neural Network With a Discontinuous Hard-Limiting Activation Function for Quadratic Programming

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.