Zhongguo Li is a lecturer in robotics and AI at the Department of Computer Science, University College London, in the U.K. His research interests focus on developing advanced optimization and learning algorithms for cooperative and competitive multi-agent systems. His research has revealed fundamental but crucial relationships among control, optimization, and learning in complex networked systems. His research not only contributes significantly to theoretical guarantees of desired optimal behaviors, but also catalyzes a number of engineering applications in optimal and sustainable scheduling of power resources and wind farms. He is one of the most active researchers in distributed optimisation and learning. In his research field, he has authored or co-authored more than 20 papers in well-recognised journals and conferences, including IEEE Transactions on Automatic Control, Automatica, IEEE Transactions on Cybernetics, and IEEE Transactions on Neural Networks and Learning Systems, among others. Dr. Li serves as an Associate Editor for Drones and Autonomous Vehicles, and a Guest Editor for Frontiers in Control Engineering. He is an active reviewer for top journals such as IEEE Transactions on Neural Networks and Learning Systems, Automatica, and IEEE Transactions on Cybernetics Zhengtao Ding is a professor of control systems in the Department of Electrical and Electronic Engineering, at the University of Manchester, in the United Kingdom. He has authored and co-authored three books, including the book Nonlinear and Adaptive Control Systems (IET, 2013), and the book Cooperative Control of Multi-Agent Systems: An Optimal and Robust Perspective (Academic Press, Elsevier, 2020). Dr. Ding has published over 300 research articles, with most of them in leading academic journals in his research area. His research interests include nonlinear and adaptive control theory and their applications, more recently network-based control, distributed optimization, and distributed machine learning, with applications to power systems and robotics. He serves as the Editor-in-Chief for Drones and Autonomous Vehicles, and the Specialty Chief Editor for Nonlinear Control for Frontiers in Control Engineering. He also serves as the Editor-in-Chief for an encyclopedia in the systems and control subject area, commissioned by Elsevier. He is an associate editor and past associate editor for a number of journals, including Scientific Reports, International Journal of Systems Sciences, Journal of Franklin Institute, IEEE Transactions on Automatic Control, among others. He is a member of the IEEE Technical Committee on Nonlinear Systems and Control, IEEE Technical Committee on Intelligent Control, and IFAC Technical Committee on Adaptive and Learning Systems. He is a Fellow of The Alan Turing Institute, the U.K.’s national institute for data science and artificial intelligence.

• Part I. Fundamental Concepts and Algorithms1. Introduction to distributed optimisation and learning2. A control perspective to single agent optimisation3. Centralised optimisation and learning4. Distributed frameworks. consensus, optimisation and learning5. Distributed unconstrained optimisation6. Constrained optimisation for resource allocation7. Non-cooperative optimisation　Part II. Advanced Algorithms and Applications8. Output regulation to time-varying optimisation9. Adaptive control to optimisation over directed graphs10. Event-triggered control to optimal coordination11. Fixed-time control to cooperative and competitive optimisation12. Robust and adaptive control to competitive optimisation13. Surrogate-model assisted algorithms to distributed optimisation14. Discrete-time algorithms for supervised learning15. Discrete-time output regulation for optimal robot coordination