This book presents research developments and novel methodologies on data-rate-constrained control and state estimation for complex networked systems with different kinds of encoding-decoding mechanisms.

Licheng Wang was a visiting Ph.D. student in the Department of Electronic and Computer Engineering at Brunel University London in the UK. From Apr. 2019 to Jun. 2022, he was a Post-Doctoral Research Fellow with the Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China. He is currently with the College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China. His research interests include nonlinear stochastic control and filtering, as well as complex networks and sensor networks.

Zidong Wang is a Professor of Dynamical Systems and Computing at Brunel University London, West London, United Kingdom. Professor Wang's research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published more than 600 papers in refereed international journals. He was awarded the Humboldt research fellowship in 1996 from Alexander von Humboldt Foundation, the JSPS Research Fellowship in 1998 from Japan Society for the Promotion of Science, and the William Mong Visiting Research Fellowship in 2002 from the University of Hong Kong. He was a recipient of the State Natural Science Award from the State Council of China in 2014 and the Outstanding Science and Technology Development Awards (once in 2005 and twice in 1997) from the National Education Committee of China.

Guoliang Wei is currently a Professor with the Business School, University of Shanghai for Science and Technology, Shanghai, China. From March 2010 to May 2011, he was an Alexander von Humboldt Research Fellow with the Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg, Germany. He has published more than 100 papers in refereed international journals. His research interests include nonlinear systems, stochastic systems, and bioinformatics.

1. Introduction 2. Gain-Scheduled State Estimation for Discrete-Time Complex Networks Under Bit-Rate Constraints 3. Partial-Neurons-Based State Estimation For Artificial Neural Networks Under Constrained Bit Rate: The Finite-Time Case 4. Synchronization Control for a Class of Discrete-Time Dynamical Networks With Packet Dropouts: A Coding-Decoding-Based Approach 5. Observer-Based Consensus Control for Discrete-Time Multi- Agent Systems with Coding-Decoding Communication Protocol 6. Recursive Filtering with Measurement Fading: A Multiple Description Coding Scheme 7. Stabilization of Linear Discrete-Time Systems Over Resource-Constrained Networks Under Dynamical Multiple Description Coding Scheme 8. An Event-Triggered Encoding Approach to Control of Linear Systems under Bit Rate Conditions 9. Event-Based State Estimation under Constrained Bit Rate: An Encoding-Decoding Approach 10. Conclusions and Future Topics