Hybrid Feedback Control Methods for Robust and Global Power Conversion

In this paper, the applicability and importance of hybrid system tools for the design of control algorithms for energy conversion in power systems is illustrated in two hybrid control designs, one pertaining to DC/DC conversion and the other to DC/AC inversion. In particular, the mathematical models considered consist of constrained switched differential equations/inclusions that include all possible modes of operation of the systems. Furthermore, the obtained models can be analyzed and their algorithms designed using hybrid system tools so as to attain key desired properties, such as stability, forward invariance, global convergence, and robustness. We argue that hybrid system tools provide a systematic approach for analysis and controller design of power systems. In particular, hybrid system tools usually leads to power quantities that have better performance and robustness to state perturbations. Furthermore, they provide guidelines on how to tune the controller parameters based on design requirements. These factors motivate the implementation of the proposed hybrid controllers in modern power conversion systems that use renewable energy sources. Simulations illustrating the main results and benchmark tests are included

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig