Suboptimal Nonlinear Predictive Control Based on Neural Wiener Models

DOI10.1007/978-3-540-85776-1_40zbMath1169.93375OpenAlexW1607747548MaRDI QIDQ3534668

Maciej Ławryńczuk

Publication date: 4 November 2008

Published in: Artificial Intelligence: Methodology, Systems, and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-85776-1_40

zbMATH Keywords

quadratic programmingneural networksoptimisationmodel predictive controlWiener modelsprocess controllinearisation

Mathematics Subject Classification ID

Quadratic programming (90C20) Control/observation systems involving computers (process control, etc.) (93C83)

Cites Work

• Identification of nonlinear systems using neural networks and polynomial models. A block-oriented approach.
• Advanced control of industrial processes. Structures and algorithms.
• Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models
• Neural networks for modelling and control of dynamic systems. A practitioner's handbook
• A Family of Model Predictive Control Algorithms With Artificial Neural Networks
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