Multilayer feedforward networks are universal approximators
Neural Networks
Properties of generalized predictive control
Automatica (Journal of IFAC) - Identification and systems parameter estimation
Advances in neural information processing systems 2
On the choice of the horizon in long-range predictive control—some simple criteria
Automatica (Journal of IFAC)
Nonlinear model-based control using second-order Volterra models
Automatica (Journal of IFAC)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Process Modeling,Simulation and Control for Chemical Engineers
Process Modeling,Simulation and Control for Chemical Engineers
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
A Family of Model Predictive Control Algorithms With Artificial Neural Networks
International Journal of Applied Mathematics and Computer Science
Suboptimal nonlinear predictive control with structured neural models
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Training of neural models for predictive control
Neurocomputing
Nonlinear predictive control based on neural multi-models
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Adaptive Modeling of Reliability Properties for Control and Supervision Purposes
International Journal of Applied Mathematics and Computer Science - Issues in Advanced Control and Diagnosis
| Hi-index | 0.01 |
This paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction accuracy than the corresponding classical Nonlinear Auto Regressive with eXternal input (NARX) models. The described suboptimal MPC algorithm needs solving on-line only a quadratic programming problem. Nevertheless, it gives closed-loop control performance similar to that obtained in fully-fledged nonlinear MPC, which hinges on online nonconvex optimisation. In order to demonstrate the advantages of structured models as well as the accuracy of the suboptimal MPC algorithm, a polymerisation reactor is studied.