In recent years technological advancements in the design and fabrication of integrated circuits have led to the development of cost effective, low power, thumb-size devices that can be used for sensing/actuating, communication, and computing. This trend is enabling a surge of new applications for which pervasive network architectures are being developed. A key feature of these systems is that they are decentralized and communication among different subsystems may be unreliable. From an engineering viewpoint, to ensure correct operation, the theoretical analysis requires a fundamental paradigm shift, as many of the typical assumptions of systems and control theory cease to hold.
Distributed Control and Filtering for Industrial Systems provides an introduction to the control and filtering algorithms devised for distributed environments, with a particular emphasis on industrial applications
Distributed Control and Filtering for Industrial Systems provides an invaluable introduction to this topic for researchers and engineers in the systems, control and communication community and can also serve as complementary reading for elective courses for distributed control and estimation at the post-graduate level.
• Distributed Model Predictive Control
• Distributed Observer-Based Control
• Distributed Adaptive Estimation
• Distributed Kalman Filtering
• Experimental Setups