This book details the designing of hybrid control strategies for practical systems containing time varying uncertainties, disturbances, nonlinearities, unknown parameters, unmodelled dynamics, delays, etc., concurrently. In this book, the advantages of different controllers will be brought together to produce superior control performance for the practical systems. Being aware of the advantages of adaptive controller to tackle unknown constant, time varying uncertainties and time varying disturbances, a variant of adaptive controller, namely L1 adaptive controller, is hybridized with other strategies. In this book, to facilitate optimal parameter setting of the basic L1 adaptive controller, stochastic optimization technique will be hybridized with it. The stability of the optimization technique along with the controller will be guaranteed analytically with the help of spectral radius convergence. The proposed method exhibits satisfactory exploration and exploitation capabilities. Again, this book will throw light on tackling nonlinearities along with uncertainties and disturbances by hybridizing fuzzy logic with L1 adaptive controller. The performances of the designed controllers will be compared with different control methodologies to validate their effectiveness. The overall stability of the nonlinear system with the designed controller will be guaranteed with the help of fuzzy Lyapunov function to retain the zonal behaviour of the system. This fuzzy PDC-L1 adaptive controller is efficient to tackle nonlinearities and at the same time cancels unknown constant, time varying uncertainties and time varying disturbances adequately. This book will also contain four simulation case studies to validate fruitfulness of the designed controllers. To demonstrate the superior control ability of these controllers in tackling practical system, three experimental case studies will also be provided.

Contains results not yet published in technical journals and conference proceedings.

Presented in a tutorial style, this comprehensive treatment unifies, simplifies, and explains most of the techniques for designing and analyzing adaptive control systems. Numerous examples clarify procedures and methods. 1995 edition.

Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. An adaptive control system utilizes on-line identification of which either system parameter or controller parameter, which does not need a priori information about the bounds on these uncertain or time-varying parameters. These approaches consider their control design in the sense of Lyapunov. Besides, there are still some branches by combining adaptive control and other control methods, i.e., nonlinear control methods, intelligent control methods, and predict control methods, to name but a few. Addresses some original contributions reporting the latest advances in adaptive control. It aims to gather the latest research on state-of-the-art methods, applications and research for the adaptive control theory, and recent new findings obtained by the technique of adaptive control. Apparently, the book cannot include all research topics. Different aspects of adaptive control are explored. Chapters includes some new tendencies and developments in research on a adaptive formation controller for multi-robot systems; L1 adaptive control design of the the longitudinal dynamics of a hypersonic vehicle model; adaptive high-gain control of biologically inspired receptor systems; adaptive residual vibration suppression of sigid-flexible coupled systems; neuro-hierarchical sliding mode control for under-actuated mechanical systems; neural network adaptive PID control design based on PLC for a water-level system; and fuzzy-based design of networked control systems with random time delays and packet dropout in the forward communication channel--

In Hubschraubern kommen mitunter aufwändige regelungstechnische Verfahren zum Einsatz, um ein intuitiv steuerbares und stabiles Verhalten der Maschine zu erzeugen. Klassische Ansätze in der Entwicklung setzen dabei auf Iterationen aus Systemidentifikation und Auslegung des Systems im Frequenzbereich. In diesem Buch wird vor dem Hintergrund der nur zu gerne unterschätzten Problematik der begrenzten Bandbreiten eine robuste adaptive Regelung eingeführt. Dazu wird ein hochfrequent-aktualisierender L1-adaptiver Regler entsprechend angepasst, ein neues adaptives Gesetz der Ausgangsrückführung eingeführt, eine neue Strategie zur Auslegung der Zustandsrückführung vorgestellt, und für den sicherheitskritischen Aspekt Effekte von Ungenauigkeiten im Rechentakt und von Sensorrauschen evaluiert. Während klassische Ansätze durch nichtlineare Optimierung weitestgehend automatisierbar sind und dennoch die Notwendigkeit wiederholter Flugtests nicht verhindern können, ist der L1-adaptive Regler bei entsprechendem Systemverständnis besonders geeignet, Entwicklungszeiten zu verkürzen. Strenge mathematische Beweise untermauern die Stabilität und Robustheit der eingeführten Algorithmen, wobei die Flugeigenschaften in einem Forschungssimulator verifiziert werden. Often, very complex controller techniques are applied to helicopters for generating an intuitively controllable and stable behavior of the aircraft. In legacy controllers, a number of iterations of system identification and loop shaping methods in frequency domain have to be conducted. In this book, a robust adaptive control theory is introduced, with the often underestimated fact of only limited available bandwidths in mind. To this end, the high-frequency adapting L1-adaptive controller is adjusted, a new adaptive law for output feedback is introduced, a new strategy for defining the design of a state feedback controller is proposed, and effects of uncertainties in the processor clock rate and of sensor noise are evaluated for taking the safety critical nature of the system into account. While legacy approaches can be automated by nonlinear optimization techniques and yet cannot eliminate the necessity of repeated flight tests, the L1-adaptive controller is particularly suitable to reduce development time, provided a sufficiently deep understanding of the system is available. Rigorous mathematical proofs substantiate the stability and robustness of the algorithms as shown, while performance and handling qualities are verified in a research simulator.