Advanced and systematic examination of the design and analysis of networked control systems and multi-agent systems

Control Over Communication Networks provides a systematic and nearly self-contained description of the analysis and design of networked control systems (NCSs) and multi-agent systems (MASs) over imperfect communication networks, with a primary focus on fading channels and delayed channels. The text characterizes the effect of communication channels on the stability and performance of NCSs, and further studies the joint impact of communication channels and network topology on the consensus of MASs.

By integrating communication and control theory, the four highly-qualified authors present fundamental results concerning the stabilization of NCSs over power-constrained fading channels and Gaussian finite-state Markov channels, linear-quadratic optimal control of NCSs with random input gains, optimal state estimation with intermittent observations, consensus of MASs with communication delay and packet dropouts, and synchronization of delayed Vicsek models.

Simulation results are given in each chapter to demonstrate the developed analysis and synthesis approaches. The references are comprehensive and up-to-date, enabling further study for readers.

Topics covered in Control Over Communication Networks include:

• Basic foundational knowledge, including control theory, communication theory, and graph theory, to enable readers to understand more complex topics
• The stabilization, optimal control, and remote state estimation problems of linear systems over channels with fading, signal-to-noise constraints, or intermittent measurements
• Consensus problems of MASs over fading/delayed channels, with directed and undirected communication graphs

Control Over Communication Networks provides a valuable unified platform for understanding the analysis and design of NCSs and MASs for researchers, control engineers working on control systems over communication networks, and mechanical engineers working on unmanned systems. Preliminary knowledge of linear system theory and matrix analysis is required.