Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Nonlinear system modeling and robust predictive control based on RBF-ARX model
Engineering Applications of Artificial Intelligence
A Family of Model Predictive Control Algorithms With Artificial Neural Networks
International Journal of Applied Mathematics and Computer Science
Dynamic matrix control algorithm based on interpolated step response neural models
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Predictive control of a distillation column using a control-oriented neural model
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Hi-index | 0.00 |
This paper is concerned with a nonlinear Dynamic Matrix Control (DMC) algorithm in which measured disturbances are compensated. Neural networks are used to calculate on-line step response coefficients for the current operating point. Such models are obtained easily off-line, no recurrent training is necessary. The algorithm is computationally efficient since the optimal future control policy is determined on-line from an easy to solve quadratic programming problem and the model is not linearised on-line. It is shown that when applied to a significantly nonlinear process the algorithm offers good control accuracy (both trajectory tracking and disturbance compensation tasks are considered).