Industrial PID Controller Tuning presents a different view of the servo/regulator compromise that has been studied for a long time in industrial control research. Optimal tuning generally involves comparison of cost functions (e.g., a quadratic function of the error or a time-weighted absolute value of the error) but without taking advantage of available multi-objective optimization methods. The book ** does make use of multi-objective optimization to account for several sources of disturbance, applying them to a more realistic problem: how to select the tuning of a controller when both servo and regulator responses are important.

The authors review the different deterministic multi-objective optimization methods. In order to ameliorate the consequences of the computational expense typically involved in their usespecifically the generation of multiple solutions among which the control engineer still has to choosealgorithms for two-degree-of-freedom PID control are implemented in MATLAB®. MATLAB code and a MATLAB-compatible program are provided for download and will help readers to adapt the ideas presented in the text for use in their own systems. Further practical guidance is offered by the inclusion of several examples of common industrial processes amenable to the use of the authors' methods. Researchers interested in non-heuristic approaches to controller tuning or in decision-making after a Pareto set has been established and graduate students interested in beginning a career working with PID control and/or industrial controller tuning will find this book a valuable reference and source of ideas.

Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

A good review of different scalarization methods helps the reader to solve multi-objective optimization problems Provides the practitioner with examples of methods put into practice in common industrial processes Readers can save time and effort with the associated MATLAB®-based application available for download with source code and database of optimal tunings Includes supplementary material: sn.pub/extras

Autorentext

José David Rojas Fernández gained his PhD in 2011. The main focus of his research is the design and analysis of control devices for complex dynamical systems. He has worked on models and simulations of wastewater treatment plants, anaerobic biodigesters and electrochemical processes used in renewable energy predictive control. He has experience using novel control strategies such as data-driven, optimal and model predictive control. Dr. Rojas's current research includes the application of modeling, simulations and control to energy generation and the design of low-cost control systems for agricultural applications. Orlando Arrieta received Bachelor's and Master's degrees in Electrical Engineering from the University of Costa Rica in 2003 and 2006 respectively. In 2007 he obtained a Master's degree in Systems Engineering and Automatic and in 2010 he received a Ph.D., both from the Autonomous University of Barcelona, Spain, in the field of PID Control. Between 2003 and 2005, he was an Interim Professor in the Automation Department of the School of Electrical Engineering of the University of Costa Rica. Later he became part of the research group in Advanced Control Systems of the Autonomous University of Barcelona, where he also conducts a postdoctoral period. Since 2011, he is Professor of the Automation Department of the School of Electrical Engineering of the University of Costa Rica, reaching in 2015 the rank of Professor. He is a researcher at the Institute of Engineering Research (INII) and the Control Engineering Research Laboratory (CERLab). His research interests are focused on Process Control, applied to the PID Control. Since 2017 he is also Dean of the Faculty of Engineering. Ramón Vilanova was born in Lérida, Spain, on September 10, 1968. He graduated in the Autonomous University of Barcelona (1991) obtaining the title of doctor through the same University (1996). At present he occupies the position of Lecturer at the School of Engineering of the Autonomous University of Barcelona where he develops educational tasks teaching subjects of Signals and Systems, Automatic Control and Technology of Automated Systems. His research interests include methods of tuning of PID regulators, systems with uncertainty, analysis of control systems with several degrees of freedom, application to environmental systems and development of methodologies for design of machine-man interfaces. He is author of several book chapters and has more than 100 publications in international congresses/journals. He is a member of IEEE and SIAM.

Inhalt
Introduction.- Process Control as a Multi-Objective Problem.- Multi-Objective Optimization Methods.- Implementation of the Multi-Objective Optimization Methods Using MATLAB®.- Application Examples.