Michael J. Grimble – författare

Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems presents a comprehensive introduction to the use of frequency domain and polynomial system design techniques for a range of industrial control and signal processing applications. The solution of stochastic and robust optimal control problems is considered, building up from single-input problems and gradually developing the results for multivariable design of the later chapters. In addition to cataloguing many of the results in polynomial systems needed to calculate industrial controllers and filters, basic design procedures are also introduced which enable cost functions and system descriptions to be specified in order to satisfy industrial requirements. Providing a range of solutions to control and signal processing problems, this book: * Presents a comprehensive introduction to the polynomial systems approach for the solution of H_2 and H_infinity optimal control problems.* Develops robust control design procedures using frequency domain methods.* Demonstrates design examples for gas turbines, marine systems, metal processing, flight control, wind turbines, process control and manufacturing systems.* Includes the analysis of multi-degrees of freedom controllers and the computation of restricted structure controllers that are simple to implement.* Considers time-varying control and signal processing problems.* Addresses the control of non-linear processes using both multiple model concepts and new optimal control solutions. Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems is essential reading for professional engineers requiring an introduction to optimal control theory and insights into its use in the design of real industrial processes. Students and researchers in the field will also find it an excellent reference tool.

Bridging the gap between research and industry, this volume systematically and comprehensively presents the latest advances in control and estimation. With emphasis on applications, industrial problems illustrate the use of transfer function and state space methods for modelling and design.Combining theroy with practice, Industrial Control Systems Design will appeal to practising engineers and academic researchers in control engineering.This unique reference:* spans fundamental state space and polynomial systems theory and introduces quantitative feedback theory.* Includes design case studies with illustrative problem descriptions and analysis from the steel, marine, process control, aerospace and power generation sectors.* Focuses on the challenges in predictive optimal control, now an indispensable method in advanced control applications.* Provides an introduction to safety-critical control systems design and combined fault monitoring and control techniques.* Discusses the design of LQG and H-controllers with several degrees of freedom, including feedback, tracking and feedforward functions.

Many large-scale processes like refineries or power generation plant are constructed using the multi-vendor system and a main co-ordinating engineering contractor. With such a methodology. the key process units are installed complete with local proprietary control systems in place. Re-assessing the so called lower level control loop design or structure is becoming less feasible or desirable. Consequently, future comp~titive gains in large-scale industrial systems will arise from the closer and optimised global integration of the process sub-units. This is one of the inherent commercial themes which motivated the research reported in this monograph. To access the efficiency and feasibility of different large-scale system designs, the traditional tool has been the global steady-state analysis and energy balance. The process industries have many such tools encapsu­ lated as proprietary design software. However, to obtain a vital and critical insight into global process operation a dynamic model and simulation is necessary. Over the last decade, the whole state of the art in system simulation has irrevocably changed. The Graphical User Interface (G UI) and icon based simulation approach is now standard with hardware platforms becoming more and more powerful. This immediately opens the way to some new and advanced large-scale dynamic simulation developments. For example, click-together blocks from standard or specialised libraries of process units are perfectly feasible now.

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; 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.