This book develops a coherent and quite general theoreticalapproach to algorithm design for iterative learning control based on the use ofoperator representations and quadratic optimization concepts including therelated ideas of inverse model control and gradient-based design.
Using detailed examples taken from linear, discreteand continuous-time systems, the author gives the reader access to theoriesbased on either signal or parameter optimization. Although the two approachesare shown to be related in a formal mathematical sense, the text presents themseparately as their relevant algorithm design issues are distinct and give riseto different performance capabilities.
Together with algorithm design, the text demonstrates theunderlying robustness of the paradigm and also includes new controllaws that are capable of incorporating input and output constraints,enable the algorithm to reconfigure systematically in order to meet therequirements of different reference and auxiliary signals and also to supportnew properties such as spectral annihilation.
Iterative LearningControl will interestacademics and graduate students working in control who will find it a usefulreference to the current status of a powerful and increasingly popular methodof control. The depth of background theory and links to practical systems willbe of use to engineers responsible for precision repetitive processes.
Professor Owens has 40 years of experience of Control Engineering theory and applications in areas including nuclear power, robotics and mechanical test. He has extensive teaching experience at both undergraduate and postgraduate levels in three UK universities. His research has included multivariable frequency domain theory and design, the theory of multivariable root loci, contributions to robust control theory, theoretical methods for controller design based on plant step data and involvement in aspects of adaptive control, model reduction and optimization-based design. His area of research that specifically underpins the text is his experience of modelling and analysis of systems with repetitive dynamics. Originally arising in control of underground coal cutters, my theory of “multipass processes” (developed in 1976 with follow-on applications introduced by J.B. Edwards) laid the foundation for analysis and design in this area and others including metal rolling and automated agriculture. This work led to substantial contributions (with collaborator E. Rogers and others) in the area of repetitive control systems (as part of 2D systems theory) but more specifically, since 1996, in the area of iterative learning control when I introduced the use of optimization to the ILC community in the form of “norm optimal iterative learning control”. Since that time he has continued to teach and research in areas related to this topic adding considerable detail and depth to the approach and introducing the ideas of parameter optimal iterative learning to simplify the implementations. This led to his development of a wide range of new algorithms, supporting analysis and applications to mechanical test. This work is also being applied to the development of data analysis tools for control in gantry robots and stroke rehabilitation equipment by collaborators at Southampton University. Work with S. Daley has also seen applications in automative test at Jaguar and related industrial sites.
David Owens was elected a Fellow of the Royal Academy of Engineering for his contributions to knowledge in these and other areas.
