Introduction to Control System Design
Kwatny, Harry
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Introduction to Control System Design equips students with the basic concepts, tools, and knowledge they need to effectively design automatic control systems. The text not only teaches readers how to design a control system, it inspires them to innovate and expand current methods to address new automation technology challenges and opportunities.
The text is designed to support a two-quarter/semester course and is organized into two main parts. Part I covers basic linear system analysis and model-assembly concepts. It presents readers with a short history of control system design and introduces basic control concepts using first-order and second order-systems. Additional chapters address the modeling of mechanical and electrical systems, as well as assembling complex models using subsystem interconnection tools.
Part II focuses on linear control system design. Students learn the fundamentals of feedback control systems; stability, regulation, and root locus design; time delay, plant uncertainty, and robust stability; and state feedback and linear quadratic optimization. The final chapter covers observer theory and output feedback control and reformulates the linear quadratic optimization problem as the more general H2 problem.
Harry Kwatny is the S. Herbert Raynes Professor of Mechanical Engineering in the Department of Mechanical Engineering and Mechanics at Drexel University. He holds a Ph.D. in electrical engineering from the University of Pennsylvania. Dr. Kwatny's research interests include control systems, symbolic computing, electric power systems, flight dynamics and control, switching and hybrid control systems.
Bor-Chin Chang is a professor in the Department of Mechanical Engineering and Mechanics at Drexel University. He holds a Ph.D. in electrical engineering from Rice University. Dr. Chang's research interests include robust control of nonlinear systems, real-time digital control, flight safety control system for unmanned aerial vehicles, and cooperative estimation/control of multi-agent systems.
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