Nonlinear Industrial Control Systems
Optimal Polynomial Systems and State-Space Approach
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Beschreibung
Nonlinear Industrial Control Systems presents a range of mostly optimisation-based methods for severely nonlinear systems; it discusses feedforward and feedback control and tracking control systems design. The plant models and design algorithms are provided in a MATLAB® toolbox (downloadable from www.springer.com/978-1-4471-7455-4) that enable both academic examples and industrial application studies to be repeated and evaluated, taking into account practical application and implementation problems.
The text makes nonlinear control theory accessible to readers having only a background in linear systems, and concentrates on real applications of nonlinear control. It covers:
- different ways of modelling nonlinear systems including state space, polynomial-based, linear parameter varying, state-dependent and hybrid;
- design techniques for nonlinear optimal control including generalised-minimum-variance, model predictive control, quadratic-Gaussian, factorised and H∞ design methods;
- design philosophies that are suitable for aerospace, automotive, marine, process-control, energy systems, robotics, servo systems and manufacturing;
- steps in design procedures that are illustrated in design studies to define cost-functions and cope with problems such as disturbance rejection, uncertainties and integral wind-up; and
- baseline non-optimal control techniques such as nonlinear Smith predictors, feedback linearization, sliding mode control and nonlinear PID.
Nonlinear Industrial Control Systems is valuable to engineers in industry dealing with actual nonlinear systems. It provides students with a comprehensive range of techniques and examples for solving real nonlinear control design problems.
Nonlinear Industrial Control Systems presents a range of mostly optimisation-based methods for severely nonlinear systems; it discusses feedforward and feedback control and tracking control systems design. The plant models and design algorithms are provided in a MATLAB® toolbox that enable both academic examples and industrial application studies to be repeated and evaluated, taking into account practical application and implementation problems.
The text makes nonlinear control theory accessible to readers having only a background in linear systems, and concentrates on real applications of nonlinear control. It covers:
- different ways of modelling nonlinear systems including state space, polynomial-based, linear parameter varying, state-dependent and hybrid;
- design techniques for nonlinear optimal control including generalised-minimum-variance, model predictive control, quadratic-Gaussian, factorised and H∞ design methods;
- design philosophies that are suitable for aerospace, automotive, marine, process-control, energy systems, robotics, servo systems and manufacturing;
- steps in design procedures that are illustrated in design studies to define cost-functions and cope with problems such as disturbance rejection, uncertainties and integral wind-up; and
- baseline non-optimal control techniques such as nonlinear Smith predictors, feedback linearization, sliding mode control and nonlinear PID.
Nonlinear Industrial Control Systems is valuable to engineers in industry dealing with actual nonlinear systems. It provides students with a comprehensive range of techniques and examples for solving real nonlinear control design problems.
Shows industrial engineers how to apply nonlinear control theory in real-world systems Teaches students a comprehensive range of simple-to-apply techniques for nonlinear control MATLAB toolbox with plant models, design procedures and industrial application examples that can be rerun or modified Includes supplementary material: sn.pub/extras
Autor*in
Michael J. Grimble
Themen in »Nonlinear Industrial Control Systems«
Aerospace Engineering Automotive Control Control Control Applications Control Engineering MATLAB® Maritime Engineering NGMV Nonlinear Control Nonlinear Generalized Minimum Variance Nonlinear Systems OJ0000 Optimal Control Optimisation Process Control
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“The book is intended for students and scientists and should be of interest to practicing and research engineers as well as PH.D. students in the area of nonlinear control design.” (Clementina Mladenova, zbMATH 1485.93003, 2022)
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