(Download) "Nonlinear Industrial Control Systems" by Michael J. Grimble & Pawel Majecki " Book PDF Kindle ePub Free
eBook details
- Title: Nonlinear Industrial Control Systems
- Author : Michael J. Grimble & Pawel Majecki
- Release Date : January 19, 2020
- Genre: Engineering,Books,Professional & Technical,Science & Nature,Chemistry,Mathematics,
- Pages : * pages
- Size : 51962 KB
Description
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; andbaseline 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.
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