Multilayer feedforward networks are universal approximators
Neural Networks
Properties of generalized predictive control
Automatica (Journal of IFAC) - Identification and systems parameter estimation
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
Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry
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
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Engineering Applications of Artificial Intelligence
A Family of Model Predictive Control Algorithms With Artificial Neural Networks
International Journal of Applied Mathematics and Computer Science
Training of neural models for predictive control
Neurocomputing
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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This paper shows control accuracy and computational efficiency of suboptimal model predictive control (MPC) based on neural models. The algorithm uses on-line a neural model of the process to determine its local linear approximation and a nonlinear free trajectory. Unlike the fully-fledged nonlinear MPC technique, which hinges on non-convex optimisation, thanks to linearisation the suboptimal algorithm requires solving on-line only a quadratic optimisation problem. Two nonlinear processes are considered: a polymerisation reactor and a distillation column. In the first case MPC based on a linear model is unstable, in the second case it is slow. It is demonstrated that the suboptimal algorithm in comparison to the nonlinear MPC with full nonlinear optimisation: (a) results in similar closed-loop control performance and (b) significantly reduces the computational burden.