Titlebook: Nonlinear Industrial Control Systems; Optimal Polynomial S Michael J. Grimble,Pawe? Majecki Book 2020 Springer-Verlag London Ltd., part of

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Linear and Nonlinear Predictive Optimal Controlon predictive control and discusses the implicit and explicit form of the algorithms. The unconstrained solution to the linear system problem is first presented, which is similar to that for most predictive control approaches. The more interesting solution is presented in the second part of the chap

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BAIT

/State-Dependent Nonlinear Predictive Optimal Controlrm. However, in this case the model predictive control problem is considered with some enhancements including, for example, a connection matrix defining the pattern of control moves allowed in the prediction horizon. The generalized predictive controller is first considered for the plant without the

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Nonlinear Industrial Process and Power Control Applications using wind energy, and metal processing involving rolling processes. It also includes design studies on process control applications involving a heavy oil fractionator, control of stirred tank reactors and finally predictive control for evaporators. The main message from the results in this chapter

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Autobiography

Paris Dauphine, and Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris. In addition to many papers on Bayesian statistics, simulation methods, and decision theory, he has writ