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Random fading communication is a type of attenuation damage of data over certain propagation media. Establishing a systematic framework for the design and analysis of learning control schemes, the book studies in depth the iterative learning control for stochastic systems with random fading communication. The authors introduce both cases where the statistics of the random fading channels are known in advance and unknown. They then extend the framework to other systems, including multi-agent systems, point-to-point tracking systems, and multi-sensor systems. More importantly, a learning control scheme is established to solve the multi-objective tracking problem with faded measurements, which can help practical applications of learning control for high-precision tracking of networked systems. The book will be of interest to researchers and engineers interested in learning control, data-driven control, and networked control systems.
Dong Shen is a Professor at the School of Mathematics, Renmin University of China, Beijing, China. His research interests include iterative learning control, stochastic optimization, and distributed artificial intelligence. Xinghuo Yu is the Distinguished Professor, a Vice-Chancellor's Professorial Fellow, and an Associate Deputy Vice-Chancellor at the Royal Melbourne Institute of Technology (RMIT University), Melbourne, Australia. He is a Fellow of the Australian Academy of Science, an Honorary Fellow of Engineers Australia, and a Fellow of the IEEE and several other professional associations.
1. Introduction SECTION I Known Channel Statistics 2. Learning Control Over Random Fading Channel 3. Tracking Performance Enhancement by Input Averaging 4. Averaging Techniques for Balancing Learning and Tracking Abilities SECTION II Unknown Channel Statistics 5. Gradient Estimation Method for Unknown Fading Channels 6. Iterative Estimation Method for Unknown Fading Channels 7. Learning-Tracking Framework Under Unknown Nonrepetitive Channel Randomness SECTION III Extensions of Systems and Problems 8. Learning Consensus with Faded Neighborhood Information 9. Point-to-Point Tracking with Fading Communications 10. Point-to-Point Tracking Using Reference Update Strategy 11. Multi-Objective Learning Tracking with Faded Measurements
