Nonlinear Control and Filtering Using Differential Flatness Approaches

This monograph presents recent advances in differential flatness theory and analyzes its use for nonlinear control and estimation. It shows how differential flatness theory can provide solutions to complicated control problems, such as those appearing in highly nonlinear multivariable systems and distributed-parameter systems. Furthermore, it shows that differential flatness theory makes it possible to perform filtering and state estimation for a wide class of nonlinear dynamical systems and provides several descriptive test cases. The book focuses on the design of nonlinear adaptive controllers and nonlinear filters, using exact linearization based on differential flatness theory. The adaptive controllers obtained can be applied to a wide class of nonlinear systems with unknown dynamics, and assure reliable functioning of the control loop under uncertainty and varying operating conditions. The filters obtained outperform other nonlinear filters in terms of accuracy of estimation and computation speed. The book presents a series of application examples to confirm the efficiency of the proposed nonlinear filtering and adaptive control schemes for various electromechanical systems. These include:· industrial robots;· mobile robots and autonomous vehicles;· electric power generation;· electric motors and actuators;· power electronics;· internal combustion engines;· distributed-parameter systems; and· communication systems.Differential Flatness Approaches to Nonlinear Control and Filtering will be a useful reference for academic researchers studying advanced problems in nonlinear control and nonlinear dynamics, and for engineers working on control applications in electromechanical systems.

Exploits the strengths of differential flatness to produce more accurate and computationally quick nonlinear filters Extends the class of nonlinear systems that can be treated with adaptive neuro-fuzzy control Simulations demonstrate the potential power of new filters and adaptive controllers in a wide range of electromechanical systems Includes supplementary material: sn.pub/extras

Autorentext
Dr. G. Rigatos, obtained a diploma (1995) and a Ph.D. (2000) both from the Department of Electrical and Computer Engineering, of the National Technical University of Athens (NTUA), Greece. Currently he holds a Researcher position (Grade B) at the Industrial Systems Institute (Greek Secretariat for Research and Technology), on the topic of Modelling and Control of Industrial Systems. In 2001 he was a post-doctoral researcher at the Institut de Recherche en Informatique et Systèmes Aléatoires IRISA, in Rennes France. In 2007 he was an invited professor (maître des conférences) at Université Paris XI (Institut d' Electronique Fondamentale). In 2012 he held a Lecturer Position at the Department of Engineering, of Harper-Adams University College, in Shropshire, UK on the topic of Mechatronics and Artificial Intelligence". He has also been an adjunct professor in Greek Universities where he has taught courses on systems and control theory. His research interests include the areas of control and robotics, optimization and fault diagnosis, adaptive systems and computational intelligence. He is a member of the IEEE, IET and IMACS.

Inhalt
Nonlinear dynamical systems and global linearizing control methods.- Differential flatness theory and flatness-based control.- Nonlinear adaptive control based on differential flatness theory.- Nonlinear Kalman Filtering based on differential flatness theory.- Differential flatness theory and industrial robotics.- Differential flatness theory in mobile robotics and autonomous vehicles.- Differential flatness theory and electric power generation.- Differential flatness theory for electric motors and actuators.- Differential flatness theory in Power Electronics.- Differential flatness theory for internal combustion engines.- Differential flatness theory for chaotic dynamical systems.- Differential flatness theory for distributed parameter systems.- Differential flatness theory in the background of other control methods.