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.
After motivating examples, this monograph gives substantial new results on the analysis and control of linear repetitive processes. 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. It presents the development of a major bank of results on the structure and design of control laws, including the case when there is uncertainty in the process model description, together with numerically reliable computational algorithms. Finally, the application of some of these results in the area of iterative learning control is treated --- including experimental results from a chain conveyor system and a gantry robot system.
The Titan II ICBM (intercontinental ballistic missile) program was developed by the United States military to bolster the size, strength, and speed of the nation’s strategic weapons arsenal in the 1950s and 1960s. Each missile carried a single warhead—the largest in U.S. inventory—used liquid fuel propellants, and was stored and launched from hardened underground silos. The missiles were deployed at basing facilities in Arkansas, Arizona, and Kansas and remained in active service for over twenty years. Since military deactivation in the early 1980s, the Titan II has served as a reliable satellite launch vehicle. This is the richly detailed story of the Titan II missile and the men and women who developed and operated the system. David K. Stumpf uses a wide range of sources, drawing upon interviews with and memoirs by engineers and airmen as well as recently declassified government documents and other public materials. Over 170 drawings and photographs, most of which have never been published, enhance the narrative. The three major accidents of the program are described in detail for the first time using authoritative sources. Titan II will be welcomed by librarians for its prodigious reference detail, by technology history professionals and laymen, and by the many civilian and Air Force personnel who were involved in the program—a deterrent weapons system that proved to be successful in defending America from nuclear attack.
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