Iterative Learning Control: An Optimization Paradigm (Advances in Industrial Control)
| Author | : | |
| Rating | : | 4.16 (554 Votes) |
| Asin | : | 1447167708 |
| Format Type | : | paperback |
| Number of Pages | : | 456 Pages |
| Publish Date | : | 2017-08-12 |
| Language | : | English |
DESCRIPTION:
David Owens was elected a Fellow of the Royal Academy of Engineering for his contributions to knowledge in these and other areas. 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. His research has included multivariable frequency domain theory and design, the theory of multiva
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. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. From the Back CoverThis book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either sig
The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.. 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. 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 read