Multilayer feedforward networks are universal approximators
Neural Networks
On the choice of the horizon in long-range predictive control—some simple criteria
Automatica (Journal of IFAC)
Parameter identification of discontinuous Hammerstein systems
Automatica (Journal of IFAC)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
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
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)
A Family of Model Predictive Control Algorithms With Artificial Neural Networks
International Journal of Applied Mathematics and Computer Science
Identification of MIMO Hammerstein models using least squares support vector machines
Automatica (Journal of IFAC)
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This paper describes a computationally efficient nonlinear Model Predictive Control (MPC) algorithm in which the neural Hammerstein model is used. The Multiple-Input Multiple-Output (MIMO) dynamic model contains a neural steady-state nonlinear part in series with a linear dynamic part. The model is linearized on-line, as a result the MPC algorithm requires solving a quadratic programming problem, the necessity of nonlinear optimization is avoided. A neutralization process is considered to discuss properties of neural Hammerstein models and to show advantages of the described MPC algorithm. In practice, the algorithm gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimization.