Nonlinear predictive control of dynamic systems represented by Wiener-Hammerstein models

DOI10.1007/s11071-016-2957-0zbMath1349.90665OpenAlexW2514563055WikidataQ59473729 ScholiaQ59473729MaRDI QIDQ345940

Maciej Ławryńczuk

Publication date: 2 December 2016

Published in: Nonlinear Dynamics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11071-016-2957-0

zbMATH Keywords

optimisationmodel predictive controlprocess controllinearisationWiener-Hammerstein systems

Mathematics Subject Classification ID

Quadratic programming (90C20) Nonlinear systems in control theory (93C10) Linearizations (93B18)

Related Items (9)

An evolving concept in the identification of an interval fuzzy model of Wiener-Hammerstein nonlinear dynamic systems ⋮ Bayesian nonparametric identification of Wiener systems ⋮ Filled function method for nonlinear model predictive control ⋮ A novel learning algorithm of the neuro-fuzzy based Hammerstein-Wiener model corrupted by process noise ⋮ WH-EA: an evolutionary algorithm for Wiener-Hammerstein system identification ⋮ Nonlinear model predictive control based on piecewise linear Hammerstein models ⋮ Interval variable step-size spline adaptive filter for the identification of nonlinear block-oriented system ⋮ Wiener system identification by input injection method ⋮ Two-step output feedback predictive control for Hammerstein systems with networked-induced time delays

Uses Software

• Wiener-Hammerstein Benchmark

Cites Work

• Unnamed Item
• Identification of nonlinear systems using neural networks and polynomial models. A block-oriented approach.
• Advanced control of industrial processes. Structures and algorithms.
• Block-oriented nonlinear system identification
• Computationally efficient model predictive control algorithms. A neural network approach
• Model-based predictive control for Hammerstein?Wiener systems

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