M. Athans

Bio: M. Athans is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Hierarchical control system & Scale (ratio). The author has an hindex of 1, co-authored 1 publications receiving 1083 citations.

Survey of decentralized control methods for large scale systems

Abstract: This paper surveys the control theoretic literature on decentralized and hierarchical control, and methods of analysis of large scale systems.

Distributed model predictive control

Abstract: The article presents results for distributed model predictive control (MPC), focusing on i) the coordination of the optimization computations using iterative exchange of information and ii) the stability of the closed-loop system when information is exchanged only after each iteration. Current research is focusing on general methods for decomposing large-scale problems for distributed MPC and methods for guaranteeing stability when multiple agents are controlling systems subject to abrupt changes.

Control and Communication Challenges in Networked Real-Time Systems

Abstract: A current survey of the emerging field of networked control systems is provided. The aim is to introduce the fundamental issues involved in designing successful networked control systems, to provide a snapshot assessment of the current state of research in the field, to suggest useful future research directions, and to provide a broad perspective on recent fundamental results. Reflecting the goals of the Special Issue itself, this paper surveys relevant work from the areas of systems and control, signal processing, detection and estimation, data fusion, and distributed systems. We discuss appropriate network architectures, topics such as coding for robustly stable control in the presence of time-varying channel capacity, channels with fixed versus adaptively variable data width, issues in data rate problems in nonlinear feedback problems, and problems in routing for stability and performance. In surveying current research on networked control systems, we find that recent theoretical advances and target applications are intimately intertwined. The common goal of papers in the Special Issue which follows is to describe key aspects of this relationship. We also aim to provide a bridge between networked control systems and closely related contemporary work dealing with sensor networks and wireless communication protocols

Detection with distributed sensors

Abstract: The extension of classical detection theory, based on the theory of statistical hypothesis testing, to the case of distributed sensors is discussed. The development is based on the formulation of a decentralized or team hypothesis testing problem. Theoretical results concerning the form of the optimal decision rule, examples, application to data fusion, and open problems are presented.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Abstract: Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.