Iterative Learning Control: An Optimization Paradigm (Advances in Industrial Control)

Daley has also seen applications in automative test at Jaguar and related industrial sites. 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, theor

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. 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. Together with algorithm design, the text demonstrates the underlying robustness of the paradigm and also includes new control laws&nb

Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. The rapi