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Linear and Nonlinear Predictive Optimal Control

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Nonlinear Industrial Control Systems

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Abstract

This chapter introduces the most popular modern control paradigm, namely model predictive control. The introduction includes a historical perspective on 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 chapter where a nonlinear operator term is included in the plant model. This is the last of the control chapters where polynomial system models are used to represent the linear subsystem. Stability issues are explored after the algorithms have been defined and the use of both hard and soft constraints are also considered. The importance of constraints in applications is illustrated in the ship roll stabilization multivariable control example presented.

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Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK

    Michael J. Grimble

  2. Industrial Systems and Control Limited, Glasgow, UK

    Paweł Majecki

Authors
  1. Michael J. Grimble
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  2. Paweł Majecki
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Correspondence to Michael J. Grimble .

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Grimble, M.J., Majecki, P. (2020). Linear and Nonlinear Predictive Optimal Control. In: Nonlinear Industrial Control Systems. Springer, London. https://doi.org/10.1007/978-1-4471-7457-8_7

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