David H. Owens – författare

This book develops a coherent and quite general theoretical approach to algorithm design for iterative learning control based on the use of operator representations and quadratic optimization concepts including the related ideas of inverse model control and gradient-based design.Using detailed examples taken from linear, discrete and continuous-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately as their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates the underlying robustness of the paradigm and also includes new control laws that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference and auxiliary signals and also to support new properties such as spectral annihilation.Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.

This book develops a coherent and quite general theoretical approach to algorithm design for iterative learning control based on the use of operator representations and quadratic optimization concepts including the related ideas of inverse model control and gradient-based design.Using detailed examples taken from linear, discrete and continuous-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately as their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates the underlying robustness of the paradigm and also includes new control laws that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference and auxiliary signals and also to support new properties such as spectral annihilation.Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.

This book introduces an optimal iterative learning control (ILC) design framework from the end user's point of view. Its central theme is the understanding of model dynamics, the construction of a procedure for systematic input updating and their contribution to successful algorithm design. The authors discuss the many applications of ILC in industrial systems, applications such as robotics and mechanical testing.The text covers a number of optimal ILC design methods, including gradient-based and norm-optimal ILC. Their convergence properties are described and detailed design guidelines, including performance-improvement mechanisms, are presented. Readers are given a clear picture of the nature of ILC and the benefits of the optimization-based approach from the conceptual and mathematical foundations of the problem of algorithm construction to the impact of available parameters in making acceleration of algorithmic convergence possible. Three case studies on robotic platforms, an electro-mechanical machine, and robot-assisted stroke rehabilitation are included to demonstrate the application of these methods in the real-world. With its emphasis on basic concepts, detailed design guidelines and examples of benefits, Optimal Iterative Learning Control will be of value to practising engineers and academic researchers alike.

These include further applications of the abstract model based stability theory which, in particular, shows the critical importance to the dynamics developed of the structure of the initial conditions at the start of each new pass, the development of stability tests and performance bounds in terms of so-called 1D and 2D Lyapunov equations.

This volume contain thirty-one papers based on talks delivered at the 4th Workshop of the Nonlinear Control Network (http://www.shef/?NCN4), held at The University of She?eld, 25-28 June, 2001. We would like to emphasize one peculiarity of our ?eld: nonlinear control is an example of a theory situated at a crossroad between mathematics and engineering science. Due to this position, nonlinear control has its roots in both ?elds and, as we deeply believe, can bring new ideas and new results for both domains. The book re?ects very well this "double character" of nonlinear control theory: the reader will ?nd in it results which cover a wide varietyofproblems:startingfrompuremathematics,throughitsapplications to nonlinear feedback design, and all the way to recent industrial advances. The following delivered invited talks at the Workshop : Frank Allgower, Alessandro Astol?, Riccardo Marino Francoise Lamnabhi-Lagarrigue, Wei Lin and Malcolm Smith Altogether the book contains 31 papers and therefore it is impossible to mention here all the discussed topics.