Engineering systems operate through actuators, most of which will exhibit phenomena such as saturation or zones of no operation, commonly known as dead zones. These are examples of piecewise-affine characteristics, and they can have a considerable impact on the stability and performance of engineering systems. This book targets controller design for piecewise affine systems, fulfilling both stability and performance requirements.
The authors present a unified computational methodology for the analysis and synthesis of piecewise affine controllers, taking an approach that is capable of handling sliding modes, sampled-data, and networked systems. They introduce algorithms that will be applicable to nonlinear systems approximated by piecewise affine systems, and they feature several examples from areas such as switching electronic circuits, autonomous vehicles, neural networks, and aerospace applications.
Piecewise Affine Control: Continuous-Time, Sampled-Data, and Networked Systems is intended for graduate students, advanced senior undergraduate students, and researchers in academia and industry. It is also appropriate for engineers working on applications where switched linear and affine models are important.
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Luis Rodrigues is a professor in the department of electrical and computer engineering at Concordia University and Director of Education at the Concordia Institute of Aerospace Design & Innovation. A professional engineer, he has worked on collaborative research and development projects with companies such as Bombardier, TRU Simulation & Training, Marinvent, SII Canada, and Maplesoft. At Concordia he is the founder and director of the HYbrid CONtrol Systems (HYCONS) Laboratory.
Behzad Samadi is the founder of Nubonetics, a cloud robotics startup in Toronto. For several years, he worked as a senior research engineer at Maplesoft. His research focuses on modeling and control of automotive and robotic systems, convex optimization, piecewise polynomial systems, 3D simulation of mechatronic systems, uncertainty analysis, and model based fault detection and isolation.
Miad Moarref is currently developing perception and motion planning algorithms for autonomous vehicles. Prior to joining the automotive industry, he was a postdoctoral fellow at the University of Toronto and a recipient of the Fonds de Recherche du Québec - Nature et Technologies (FRQNT) scholarship in 2014.
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