Nonlinear Industrial Control Systems

Control. Abstract This chapter provides a very brief introduction to many areas in nonlinear systems theory and ... Control engineering plays a large part in providing safe and reliable operation of any process or industrial system.


Author: Michael J. Grimble

Publisher: Springer Nature

ISBN: 9781447174578

Category: Technology & Engineering

Page: 764

View: 385

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.

Robust Industrial Control Systems

If the system is new and does not have an existing controller then a different procedure must be followed. ... part of the chapter was concerned with the NGMV control law that was derived specifically for nonlinear industrial systems.


Author: Michael J. Grimble

Publisher: John Wiley & Sons

ISBN: 9780470020746

Category: Science

Page: 698

View: 414

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.

Nonlinear H2 H Infinity Constrained Feedback Control

This book provides techniques to produce robust, stable and useable solutions to problems of H-infinity and H2 control in high-performance, non-linear systems for the first time.


Author: Murad Abu-Khalaf

Publisher: Springer Science & Business Media

ISBN: 9781846283505

Category: Technology & Engineering

Page: 204

View: 703

This book provides techniques to produce robust, stable and useable solutions to problems of H-infinity and H2 control in high-performance, non-linear systems for the first time. The book is of importance to control designers working in a variety of industrial systems. Case studies are given and the design of nonlinear control systems of the same caliber as those obtained in recent years using linear optimal and bounded-norm designs is explained.

Nonlinear Control Systems

This text emphasizes classical methods and presents essential analytical tools and strategies for the construction and development of improved design methods in nonlinear control.


Author: Zoran Vukic

Publisher: CRC Press

ISBN: 9780203912652

Category: Technology & Engineering

Page: 408

View: 693

This text emphasizes classical methods and presents essential analytical tools and strategies for the construction and development of improved design methods in nonlinear control. It offers engineering procedures for the frequency domain, as well as solved examples for clear understanding of control applications in the industrial, electrical, proce

Fuzzy Control of Industrial Systems

When the plant/process is linear, these adjustments are independent from one another and can be tuned one-at-a-time. In the nonlinear case, however, they affect one another, and the controller becomes difficult or even impossible to ...


Author: Ian S. Shaw

Publisher: Springer

ISBN: 9781475728132

Category: Mathematics

Page: 192

View: 240

Fuzzy Control of Industrial Systems: Theory and Applications presents the basic theoretical framework of crisp and fuzzy set theory, relating these concepts to control engineering based on the analogy between the Laplace transfer function of linear systems and the fuzzy relation of a nonlinear fuzzy system. Included are generic aspects of fuzzy systems with an emphasis on the many degrees of freedom and its practical design implications, modeling and systems identification techniques based on fuzzy rules, parametrized rules and relational equations, and the principles of adaptive fuzzy and neurofuzzy systems. Practical design aspects of fuzzy controllers are covered by the detailed treatment of fuzzy and neurofuzzy software design tools with an emphasis on iterative fuzzy tuning, while novel stability limit testing methods and the definition and practical examples of the new concept of collaborative control systems are also given. In addition, case studies of successful applications in industrial automation, process control, electric power technology, electric traction, traffic engineering, wastewater treatment, manufacturing, mineral processing and automotive engineering are also presented, in order to assist industrial control systems engineers in recognizing situations when fuzzy and neurofuzzy would offer certain advantages over traditional methods, particularly in controlling highly nonlinear and time-variant plants and processes.

Nonlinear Model based Process Control

In practice, compensation of deadtimes in control systems is very important. The Smith predictor (Smith, 1957) is a well known deadtime compensation technique. It utilizes a process model to add feedback around the conventional ...


Author: Rashid M. Ansari

Publisher: Springer Science & Business Media

ISBN: 9781447107392

Category: Science

Page: 232

View: 336

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The last decade has seen considerable interest in reviving the fortunes of non linear control. In contrast to the approaches of the 60S, 70S and 80S a very pragmatic agenda for non-linear control is being pursued using the model-based predictive control paradigm. This text by R. Ansari and M. Tade gives an excellent synthesis of this new direction. Two strengths emphasized by the text are: (i) four applications found in refinery processes are used to give the text a firm practical continuity; (ii) a non-linear model-based control architecture is used to give the method a coherent theoretical framework.

Tuning Innovation with Biotechnology

Chapter 7 Hybrid System by AINFS and AINFNNS for Robust Control of Nonlinear System 7.1 Technology History and Artificial ... faced by engineers is the control of the dynamic characteristics in highly nonlinear industrial systems.


Author: Dong Hwa Kim

Publisher: CRC Press

ISBN: 9781315340913

Category: Medical

Page: 218

View: 563

This book deals with evolving intelligence systems and their use in immune algorithm (IM), particle swarm optimization (PSO), bacterial foraging (BF), and hybrid intelligent system to improve plants, robots, etc. It discusses the motivation behind research on and background of evolving intelligence systems and illustrates IM-based approach for parameter estimation required for designing an intelligent system. It approaches optimal intelligent tuning using a hybrid genetic algorithm–particle swarm optimization (GA-PSO) and illustrates hybrid GA-PSO for intelligent tuning of vector system.

Industrial Control Electronics

An example is an analytical control system which controls the pH of a solution. To accommodate a nonlinear process, a controller which has adaptive control capabilities is used. An adaptive controller uses a combination of software ...


Author: Terry L.M. Bartelt

Publisher: Cengage Learning

ISBN: 9781285225104

Category: Technology & Engineering

Page: 656

View: 500

This new edition continues to provide state-of-the-art coverage of the entire spectrum of industrial control, from servomechanisms to instrumentation. Material on the components, circuits, instruments, and control techniques used in today's industrial automated systems has been fully updated to include new information on thyristors and sensor interfacing and updated information on AC variable speed drives. Following an overview of an industrial control loop, readers may delve into individual sections that explore each element of the loop in detail. This logical format offers the flexibility needed to use the book effectively in a variety of courses, from electric motors to servomechanisms, programmable controllers, and more! Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Advanced Control of Chemical Processes 1994

APPLICATION OF GEOMETRIC NONLINEAR CONTROL IN THE PROCESS INDUSTRIES - A CASE STUDY S.D. DORE, J.D. PERKINS and L.S. KERSHENBAUM Centre for Process Systems Engineering, Imperial College, South Kensington, London SW7 2BY, U.K. Abstract.


Author: D. Bonvin

Publisher: Elsevier

ISBN: 9781483297590

Category: Science

Page: 560

View: 305

This publication brings together the latest research findings in the key area of chemical process control; including dynamic modelling and simulation - modelling and model validation for application in linear and nonlinear model-based control: nonlinear model-based predictive control and optimization - to facilitate constrained real-time optimization of chemical processes; statistical control techniques - major developments in the statistical interpretation of measured data to guide future research; knowledge-based v model-based control - the integration of theoretical aspects of control and optimization theory with more recent developments in artificial intelligence and computer science.

Industrial Control Systems

The book contains mathematically rigorous models and techniques generally applicable to control systems with specific orientation toward industrial systems.


Author: Adedeji B. Badiru

Publisher: CRC Press

ISBN: 9781420075588

Category: Technology & Engineering

Page: 380

View: 561

Issues such as logistics, the coordination of different teams, and automatic control of machinery become more difficult when dealing with large, complex projects. Yet all these activities have common elements and can be represented by mathematics. Linking theory to practice, Industrial Control Systems: Mathematical and Statistical Models and Techniques presents the mathematical foundation for building and implementing industrial control systems. The book contains mathematically rigorous models and techniques generally applicable to control systems with specific orientation toward industrial systems. An amalgamation of theoretical developments, applied formulations, implementation processes, and statistical control, the book covers: Industrial innovations and systems analysis Systems fundamentals Technical systems Production systems Systems filtering theory Systems control Linear and nonlinear systems Switching in systems Systems communication Transfer systems Statistical experimental design models (factorial design and fractional factorial design) Response surface models (central composite design and Box–Behnken design) Examining system fundamentals and advanced topics, the book includes examples that demonstrate how to use the statistical designs to develop feedback controllers and minimum variance controller designs for industrial applications. Clearly detailing concepts and step-by-step procedures, it matches mathematics with practical applications, giving you the tools to achieve system control goals.