Titlebook: Nonlinear Predictive Control Using Wiener Models; Computationally Effi Maciej Ławryńczuk Book 2022 The Editor(s) (if applicable) and The Au

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Wiener Modelsels are detailed: one SISO case and two MIMO ones. A short review of identification methods of Wiener models is given, possible internal structures of both model parts are discussed and example applications of Wiener models are reported. Finally, other structures of cascade models are shortly mentio

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MPC of Input-Output Benchmark Wiener Processeso input-output benchmark processes. Two SISO processes are considered, the second one has complex dynamics and Laguerre parameterisation turns out to be beneficial. Next, three MIMO benchmarks are considered: two with two inputs and two outputs (without and with cross-couplings) and one with as many

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Modelling and MPC of the Neutralisation Reactor Using Wiener Modelsrtly described and identification of the Wiener model is discussed. Polynomials and neural networks are used in the nonlinear static block of the model. Effectiveness of both model classes is compared. Implementation details of different MPC algorithms are given. Next, MPC algorithms are compared in

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Modelling and MPC of the Proton Exchange Membrane Fuel Cell Using Wiener Modelsocess is shortly described, identification of three structures of neural Wiener models and model selection are discussed. Efficiency of the polynomial Wiener model is also evaluated. Implementation details of different MPC algorithms are given. Next, efficiency of MPC algorithms is compared in terms

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MPC Algorithms Using State-Space Wiener Modelsmodel is recalled and the rudimentary MPC-NO algorithm is described. Next, the computationally efficient MPC methods with on-line model linearisation are characterised: the MPC-SSL and the MPC-NPSL ones as well as two MPC schemes with on-line trajectory linearisation: the MPC-NPLT and MPC-NPLPT sche

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