This monograph demonstrates how the performance of various well-known adaptive controllers can be improved significantly using the dual effect. The modifications to incorporate dual control are realized separately and independently of the main adaptive controller without complicating the algorithms. A new bicriterial approach for dual control is developed and applied to various types of popular linear and nonlinear adaptive controllers. Practical applications of the designed controllers to several real-time problems are presented. This monograph is the first book providing a complete exposition on the dual control problem from the inception in the early 1960s to the present state of the art aiming at students and researchers in adaptive control as well as design engineers in industry.

Suitable for advanced undergraduates and graduate students, this overview introduces theoretical and practical aspects of adaptive control, with emphasis on deterministic and stochastic viewpoints. 1995 edition.

Unique in its systematic approach to stochastic systems, this book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, nonlinear dynamics, component failure, unpredictable disturbances, multi-modality and high dimensional spaces.

The authors here provide a detailed treatment of the design of robust adaptive controllers for nonlinear systems with uncertainties. They employ a new tool based on the ideas of system immersion and manifold invariance. New algorithms are delivered for the construction of robust asymptotically-stabilizing and adaptive control laws for nonlinear systems. The methods proposed lead to modular schemes that are easier to tune than their counterparts obtained from Lyapunov redesign.

Model Free Adaptive Control: Theory and Applications summarizes theory and applications of model-free adaptive control (MFAC). MFAC is a novel adaptive control method for the unknown discrete-time nonlinear systems with time-varying parameters and time-varying structure, and the design and analysis of MFAC merely depend on the measured input and output data of the controlled plant, which makes it more applicable for many practical plants. This book covers new concepts, including pseudo partial derivative, pseudo gradient, pseudo Jacobian matrix, and generalized Lipschitz conditions, etc.; dynamic linearization approaches for nonlinear systems, such as compact-form dynamic linearization, partial-form dynamic linearization, and full-form dynamic linearization; a series of control system design methods, including MFAC prototype, model-free adaptive predictive control, model-free adaptive iterative learning control, and the corresponding stability analysis and typical applications in practice. In addition, some other important issues related to MFAC are also discussed. They are the MFAC for complex connected systems, the modularized controller designs between MFAC and other control methods, the robustness of MFAC, and the symmetric similarity for adaptive control system design. The book is written for researchers who are interested in control theory and control engineering, senior undergraduates and graduated students in engineering and applied sciences, as well as professional engineers in process control.

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.

The book reviews developments in the following fields: optimal adaptive control; online differential games; reinforcement learning principles; and dynamic feedback control systems.