论文信息 - Neural network for constrained predictive control

Neural network for constrained predictive control

Presents the way in which optimization neural nets can be used to implement generalized predictive control for systems with constrained inputs and outputs. A set of recursive formulas to obtain the net parameters from the process parameters for first-order systems is given. The results obtained by simulation and electronic implementation of the neural net are presented. >

[1] M. Kawato,et al. Hierarchical neural network model for voluntary movement with application to robotics , 1988, IEEE Control Systems Magazine.

[2] E. Camacho. Constrained generalized predictive control , 1993, IEEE Trans. Autom. Control..

[3] A. Sideris,et al. A multilayered neural network controller , 1988, IEEE Control Systems Magazine.

[4] David W. Clarke,et al. Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[5] J.M. Quero,et al. Neural generalized predictive self-tuning controllers , 1990, 1990 IEEE International Conference on Systems Engineering.

[6] Leon O. Chua,et al. Neural networks for nonlinear programming , 1988 .

[7] A. Guez,et al. Neural network architecture for control , 1988, IEEE Control Systems Magazine.

[8] Eduardo F. Camacho,et al. Precomputation of generalized predictive self-tuning controllers , 1991 .

[9] John J. Hopfield,et al. Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit , 1986 .

[10] J J Hopfield,et al. Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.