Distributed Model Predictive Control Using a Chain of Tubes

A new distributed MPC algorithm for the regulation of dynamically coupled subsystems is presented in this paper. The current control action is computed via two robust controllers working in a nested fashion. The inner controller builds a nominal reference trajectory from a decentralized perspective. The outer controller uses this information to take into account the effects of the coupling and generate a distributed control action. The tube-based approach to robustness is employed. A supplementary constraint is included in the outer optimization problem to provide recursive feasibility of the overall controllerComment: Accepted for presentation at the UKACC CONTROL 2016 conference (Belfast, UK

A scalable line-independent design algorithm for voltage and frequency control in AC islanded microgrids

We propose a decentralized control synthesis procedure for stabilizing voltage and frequency in AC Islanded microGrids (ImGs) composed of Distributed Generation Units (DGUs) and loads interconnected through power lines. The presented approach enables Plug-and-Play (PnP) operations, meaning that DGUs can be added or removed without compromising the overall ImG stability. The main feature of our approach is that the proposed design algorithm is line-independent. This implies that (i) the synthesis of each local controller requires only the parameters of the corresponding DGU and not the model of power lines connecting neighboring DGUs, and (ii) whenever a new DGU is plugged in, DGUs physically coupled with it do not have to retune their regulators because of the new power line connected to them. Moreover, we formally prove that stabilizing local controllers can be always computed, independently of the electrical parameters. Theoretical results are validated by simulating in PSCAD the behavior of a 10-DGUs ImG

System Level Synthesis

This article surveys the System Level Synthesis framework, which presents a novel perspective on constrained robust and optimal controller synthesis for linear systems. We show how SLS shifts the controller synthesis task from the design of a controller to the design of the entire closed loop system, and highlight the benefits of this approach in terms of scalability and transparency. We emphasize two particular applications of SLS, namely large-scale distributed optimal control and robust control. In the case of distributed control, we show how SLS allows for localized controllers to be computed, extending robust and optimal control methods to large-scale systems under practical and realistic assumptions. In the case of robust control, we show how SLS allows for novel design methodologies that, for the first time, quantify the degradation in performance of a robust controller due to model uncertainty -- such transparency is key in allowing robust control methods to interact, in a principled way, with modern techniques from machine learning and statistical inference. Throughout, we emphasize practical and efficient computational solutions, and demonstrate our methods on easy to understand case studies.Comment: To appear in Annual Reviews in Contro

Sequence-based Anytime Control

We present two related anytime algorithms for control of nonlinear systems when the processing resources available are time-varying. The basic idea is to calculate tentative control input sequences for as many time steps into the future as allowed by the available processing resources at every time step. This serves to compensate for the time steps when the processor is not available to perform any control calculations. Using a stochastic Lyapunov function based approach, we analyze the stability of the resulting closed loop system for the cases when the processor availability can be modeled as an independent and identically distributed sequence and via an underlying Markov chain. Numerical simulations indicate that the increase in performance due to the proposed algorithms can be significant.Comment: 14 page