Neuro Fuzzy Control Of Industrial Systems With Actuator Nonlinearities

Brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities.

Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications. Neural networks capture the parallel processing and learning capabilities of biological nervous systems, and fuzzy logic captures the decision-making capabilities of human linguistics and cognitive systems.

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.

Fuzzy control methods are critical for meeting the demands of complex nonlinear systems. They bestow robust, adaptive, and self-correcting character to complex systems that demand high stability and functionality beyond the capabilities of traditional methods. A thorough treatise on the theory of fuzzy logic control is out of place on the design bench. That is why Fuzzy Controller Design: Theory and Applications offers laboratory- and industry-tested algorithms, techniques, and formulations of real-world problems for immediate implementation. With surgical precision, the authors carefully select the fundamental elements of fuzzy logic control theory necessary to formulate effective and efficient designs. The book supplies a springboard of knowledge, punctuated with examples worked out in MATLAB®/SIMULINK®, from which newcomers to the field can dive directly into applications. It systematically covers the design of hybrid, adaptive, and self-learning fuzzy control structures along with strategies for fuzzy controller design suitable for on-line and off-line operation. Examples occupy an entire chapter, with a section devoted to the simulation of an electro-hydraulic servo system. The final chapter explores industrial applications with emphasis on techniques for fuzzy controller implementation and different implementation platforms for various applications. With proven methods based on more than a decade of experience, Fuzzy Controller Design: Theory and Applications is a concise guide to the methodology, design steps, and formulations for effective control solutions.

This book provides a basic understanding of adaptive control and its applications in Flight control. It discusses the designing of an adaptive feedback control system and analyzes this for flight control of linear and nonlinear aircraft models using synthetic jet actuators. It also discusses control methodologies and the application of control techniques which will help practicing flight control and active flow control researchers. It also covers modelling and control designs which will also benefit researchers from the background of fluid mechanics and health management of actuation systems. The unique feature of this book is characterization of synthetic jet actuator nonlinearities over a wide range of angles of attack, an adaptive compensation scheme for such nonlinearities, and a systematic framework for feedback control of aircraft dynamics with synthetic jet actuators.

V VII X Table of Contents Table of Contents XI XII Table of Contents A. Taware and G. Tao: Control of Sandwich Nonlinear Systems, LNCIS 288, pp. 1-8, 2003. Springer-Verlag Berlin Heidelberg 2003 A. Taware and G. Tao: Control of Sandwich Nonlinear Systems, LNCIS 288, pp. 9-16, 2003. Springer-Verlag Berlin Heidelberg 2003 x(t), pilot valve piston position Return Pressure Source Return Load M, b y(t) A. Taware and G. Tao: Control of Sandwich Nonlinear Systems, LNCIS 288, pp. 17-28, 2003. Springer-Verlag Berlin Heidelberg 2003 load displacement, y(t) 3 2 1 0 ?1 ?2 ?3 0 10 20 30 40 50 60 70 80 90 100 solid?without DZ, dotted? with DZ Load displacement, y(t) 0.4 0.2 0 ?0.2 ?0.4 ?0.6 ?0.8 10 20 30 40 50 60 70 80 90 100 solid?without DZ, dotted?with DZ, dashdot?compensated Load displacement, y(t) 3 2 1 0 ?1 ?2 ?3 0 10 20 30 40 50 60 70 80 90 100 Control input, x(t) 0.8 0.6 0.4 0.2 0 ?0.2 ?0.4 ?0.6 0 10 20 30 40 50 60 70 80 90 100 solid?without DZ, dotted?with DZ, dashdot?compensated Load displacement, y(t) 0.6 0.4 0.2 0 ?0.2 ?0.4 ?0.6 ?0.8 0 10 20 30 40 50 60 70 80 90 100 solid?without DZ, dotted?with DZ, dashdot?compensated A. Taware and G. Tao: Control of Sandwich Nonlinear Systems, LNCIS 288, pp. 29-54, 2003. Springer-Verlag Berlin Heidelberg 2003

This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models. Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic.