Shirantha Welikala

Mohammad Javad Najafirad, Shirantha Welikala, Lei Wu et al.

This paper introduces a dissipativity-based framework for the joint design of distributed controllers and communication topologies in AC microgrids (MGs), providing robust performance guarantees for voltage regulation, frequency synchronization, and proportional power sharing across distributed generators (DGs). The closed-loop AC MG is represented as a networked system in which DGs, distribution lines, and loads function as interconnected subsystems linked through cyber-physical networks. Each DG utilizes a three-layer hierarchical control structure: a steady-state controller for operating point configuration, a local feedback controller for voltage tracking, and a distributed droop-free controller implementing normalized power consensus for frequency coordination and proportional power distribution. The operating point design is formulated as an optimization problem. Leveraging dissipativity theory, we derive necessary and sufficient subsystem dissipativity conditions. The global co-design is then cast as a convex linear matrix inequality (LMI) optimization that jointly determines distributed controller parameters and sparse communication architecture while managing the highly nonlinear, coupled dq-frame dynamics characteristic of AC systems. Simulation results from an islanded AC MG in a MATLAB/Simulink environment verify that the proposed framework achieves robust voltage regulation, frequency synchronization, and proportional power sharing through the optimized communication topology.

Mohammad Javad Najafirad, Shirantha Welikala

This paper presents a dissipativity-based distributed droop-free control and communication topology co-design framework for voltage regulation and current sharing in DC microgrids (MGs), where constant-power loads (CPLs) and voltage-source converter (VSC) input saturation introduce significant nonlinearities. In particular, CPLs introduce an inherently destabilizing nonlinearity, while VSC input saturation imposes hard amplitude constraints on applicable control input at each distributed generator (DG), collectively making the DC MG control system design extremely challenging. To this end, the DC MG is modeled as a networked system of DGs, transmission lines, and loads coupled through a static interconnection matrix. Each DG is equipped with a local PI-based controller with an anti-windup compensator and a distributed consensus-based global controller, from which a nonlinear networked error dynamics model is derived. The CPL nonlinearity is characterized via sector-boundedness with the S-procedure applied directly to yield tight LMI conditions, while the VSC input saturation is handled via a dead-zone decomposition and sector-boundedness, with both nonlinearities simultaneously absorbed into the dissipativity analysis. Both nonlinearities are simultaneously absorbed into the dissipativity analysis using the S-procedure. Subsequently, local controller gains and passivity indices, and distributed controller gains and the communication topology are co-designed by solving a sequence of local and global Linear Matrix Inequality (LMI) problems, enabling a one-shot co-design process that avoids iterative procedures. The effectiveness of the proposed framework is validated through simulation of an islanded DC MG under multiple operating scenarios, demonstrating robust performance superior to conventional control approaches.

Zihao Song, Shirantha Welikala, Panos J. Antsaklis et al.

In this paper, we consider the distributed optimal control problem for discrete-time linear networked systems. In particular, we are interested in learning distributed optimal controllers using graph recurrent neural networks (GRNNs). Most of the existing approaches result in centralized optimal controllers with offline training processes. However, as the increasing demand of network resilience, the optimal controllers are further expected to be distributed, and are desirable to be trained in an online distributed fashion, which are also the main contributions of our work. To solve this problem, we first propose a GRNN-based distributed optimal control method, and we cast the problem as a self-supervised learning problem. Then, the distributed online training is achieved via distributed gradient computation, and inspired by the (consensus-based) distributed optimization idea, a distributed online training optimizer is designed. Furthermore, the local closed-loop stability of the linear networked system under our proposed GRNN-based controller is provided by assuming that the nonlinear activation function of the GRNN-based controller is both local sector-bounded and slope-restricted. The effectiveness of our proposed method is illustrated by numerical simulations using a specifically developed simulator.