We propose a distributed control, in which many identical control agents are deployed for controlling a linear time-invariant plant that has multiple input-output channels. Each control agent can join or leave the control loop during the operation of stabilization without particular initialization over the whole networked agents. Once new control agents join the loop, they self-organize their control dynamics, which does not interfere the control by other active agents, which is achieved by local communication with the neighboring agents. The key idea enabling these features is the use of Bass' algorithm, which allows the distributed computation of stabilizing gains by solving a Lyapunov equation in a distributed manner.
The complexity arising in AC microgrids from multiple interacting distributed generation units (DGUs) with intermittent supply behavior requires local voltage-source inverters (VSIs) to be controlled in a distributed or decentralized manner at primary level. In (Strehle et al., 2019), we use passivity theory to design decentralized, plug-and-play voltage and frequency controllers for such VSIs. However, the stability analysis of the closed-loop system requires a load-connected topology, in contrast to real grids where loads are arbitrarily located. In this paper, we expand our former approach …
A plug-and-play model predictive control (PnP MPC) scheme is proposed for varying-topology networks to track piecewise constant references. The proposed scheme allows subsystems to occasionally join and leave the network while preserving asymptotic stability and recursive feasibility and comprises two main phases. In the redesign phase, passivity-based control is used to ensure that asymptotic stability of the network is preserved. In the transition phase, reconfigurable terminal ingredients are used to ensure that the distributed MPC problem is initially feasible after the PnP operation. The …
In this paper we propose a stochastic decentralized algorithm to recommend the most convenient Charging Station (CS) to Plug-in Electric Vehicles (PEVs) that need charging. In particular, we use different cost functions to describe the possibly different priorities of PEV drivers, such as the preference to minimize charging costs, charging times, or the distance between them and the CS. For this purpose, we leverage on an IoT architecture based on a permissioned Distributed Ledger Technology (DLT) to enforce compliance of drivers and reduces the occurrence of detrimental misbehaviours of drive…
Chance-Constrained ADMM Approach for Decentralized Control of Distributed Energy Resources
Tandon School of Engineering, New York University（纽约大学坦顿工程学院）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）
A decentralized scalable approach to voltage control of DC islanded microgrids
Università degli Studi di Pavia（帕维亚大学）；United Technologies Research Center Ireland（联合技术研究中心爱尔兰）；Institute of Energy Technology, Aalborg University（奥尔堡大学能源技术研究所）
The current limitation in the synthesis of distributed $\mathscr{H}_2$ controllers for linear interconnected systems is scalability due to non-convex or unstructured synthesis conditions. In this paper we develop convex and structured conditions for the existence of a distributed $\mathscr{H}_2$ controller for discrete-time interconnected systems with an interconnection structure that corresponds to an arbitrary graph. Neutral interconnections and a storage function with a block-diagonal structure are utilized to attain coupling conditions that are of a considerably lower computational complex…
The proliferation of plug-in electric vehicles (PEVs) advocates a distributed paradigm for the coordination of PEV charging. Distinct from existing primal-dual decomposition or consensus methods, this paper proposes a cutting-plane based distributed algorithm, which enables an asynchronous coordination while well preserving individual's private information. To this end, an equivalent surrogate model is first constructed by exploiting the duality of the original optimization problem, which masks the private information of individual users by a transformation. Then, a cutting-plane based algorit…
This paper shows that a relation can be found between the voltage at the terminals of an inverter-interfaced Renewable Energy Source RES and its optimal reactive power support. This relationship, known as Volt-Var Curve VVC, enables the decentral operation of RES for Active Voltage Management (AVM). In this paper, the decentralized AVM technique is modified to consider the effects of the realistic operational constraints of RES. The AVM technique capitalizes on the reactive power support capabilities of inverters to achieve the desired objective in unbalanced active Low-Voltage Distribution Sy…
Motivated by decentralized sensing and policy evaluation problems, we consider a particular type of distributed stochastic optimization problem over a network, called the online stochastic distributed averaging problem. We design a dual-based method for this distributed consensus problem with Polyak--Ruppert averaging and analyze its behavior. We show that the proposed algorithm attains an accelerated deterministic error depending optimally on the condition number of the network, and also that it has an order-optimal stochastic error. This improves on the guarantees of state-of-the-art distrib…
Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations
Institute for Energy Systems, Energy Efficiency and Energy Economics, TU Dortmund University（能源系统、能源效率与能源经济学研究所，德累斯顿理工大学）；Institute of Engineering and Computational Mechanics, University of Stuttgart（工程与计算力学研究所，斯图加特大学）
One of the major challenges of voltage stabilization in converter-based DC microgrids are the multiple interacting units displaying intermittent supply behavior. In this paper, we address this by a decentralized scalable, plug-and-play voltage controller for voltage-source converters (VSCs) at primary level. In contrast to existing approaches, we follow a systematic and constructive design based on port-Hamiltonian systems (PHSs) which does neither require the heuristic proposition of a Lyapunov function nor the computation of auxilliary variables such as time-derivatives. By employing the Ham…
Centralized Versus Decentralized Team Games of Distributed Stochastic Differential Decision Systems with Noiseless Information Structures-Part II: Applications
University of Cyprus（塞浦路斯大学）；University of Ottawa（渥太华大学）
Distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control. Significant research efforts have been devoted to developing and analyzing new algorithms for various applications. In this work, we provide a fresh perspective to understand, analyze, and design distributed optimization algorithms. Through the lens of multi-rate feedback control, we show that a wide class of distributed algorithms, including popular decentralized/federated schemes, can be viewed as discretizing a certain continuous-time feedbac…
Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications (also referred to as augmented information services), such as industrial automation and metaverse experiences (e.g., extended reality, immersive gaming). While such applications require processed streams to be shared and simultaneously consumed by multiple users/devices, existing technologies lack efficient mechanisms to deal with their inherent multicast nature, leading to unnecessary traffic redundancy …
In smart grids, distributed energy resources (DERs) have penetrated residential zones to provide a new form of electricity supply, mainly from renewable energy. Residential households and commercial buildings with DERs have become prosumers in the local grids, since they can sell surplus power to others. Researches have been initiated to integrate and utilize DERs through better control and communication strategies. With the advances in the Internet of Things (IoT) technology, unprecedented coordination among DERs can be achieved to facilitate energy trading and transactive energy management. …
John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）
This paper considers a distributed reinforcement learning problem for decentralized linear quadratic control with partial state observations and local costs. We propose a Zero-Order Distributed Policy Optimization algorithm (ZODPO) that learns linear local controllers in a distributed fashion, leveraging the ideas of policy gradient, zero-order optimization and consensus algorithms. In ZODPO, each agent estimates the global cost by consensus, and then conducts local policy gradient in parallel based on zero-order gradient estimation. ZODPO only requires limited communication and storage even i…
In this paper, we consider a large network containing many regions such that each region is equipped with a worker with some data processing and communication capability. For such a network, some workers may become stragglers due to the failure or heavy delay on computing or communicating. To resolve the above straggling problem, a coded scheme that introduces certain redundancy for every worker was recently proposed, and a gradient coding paradigm was developed to solve convex optimization problems when the network has a centralized fusion center. In this paper, we propose an iterative distri…
Decomposition of non-convex optimization via bi-level distributed ALADIN
Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院自动化与应用信息研究所）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
Model-based Systems Engineering (MBSE) has been widely utilized to formalize system artifacts and facilitate their development throughout the entire lifecycle. During complex system development, MBSE models need to be frequently exchanged across stakeholders. Concerns about data security and tampering using traditional data exchange approaches obstruct the construction of a reliable marketplace for digital assets. The emerging Distributed Ledger Technology (DLT), represented by blockchain, provides a novel solution for this purpose owing to its unique advantages such as tamper-resistant and de…
In this study, we propose a novel heuristic two-step algorithm for shared ridehailing in which users can share their rides with only one more user. The algorithm, which is centrally formulated, starts with matching users and creating a set of passenger pairs in step 1 and is followed by solving an assignment problem to assign passenger pairs to the vehicles. To solve the problem of high computational time in dynamic ride-matching problems, we propose a distributed system that is based on vehicle to infrastructure (V2I) and infrastructure to infrastructure (I2I) communication. To evaluate the d…
The advent of distributed energy resources (DERs), such as distributed renewables, energy storage, electric vehicles, and controllable loads, \rv{brings} a significantly disruptive and transformational impact on the centralized power system. It is widely accepted that a paradigm shift to a decentralized power system with bidirectional power flow is necessary to the integration of DERs. The virtual power plant (VPP) emerges as a promising paradigm for managing DERs to participate in the power system. In this paper, we develop a blockchain-based VPP energy management platform to facilitate a ric…
In the last decade, data-driven algorithms outperformed traditional optimization-based algorithms in many research areas, such as computer vision, natural language processing, etc. However, extensive data usages bring a new challenge or even threat to deep learning algorithms, i.e., privacy-preserving. Distributed training strategies have recently become a promising approach to ensure data privacy when training deep models. This paper extends conventional serverless platforms with serverless edge learning architectures and provides an efficient distributed training framework from the networkin…
Rice Univ.（莱斯大学）；Univ. of Pennsylvania（宾夕法尼亚大学）；Cambridge University（剑桥大学）
Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information. While centralized controllers are readily available, they face limitations in terms of scalability and implementation, as they do not respect the distributed information structure imposed by the network system of agents. Given the difficulties in finding optimal decentralized controllers, we propose a novel framework using graph neural networks (GNNs) to \emph{learn} these controllers. GNNs are well-suited for the task since they are naturally …
Texas A&M University（德克萨斯农工大学）；University of Minnesota（明尼苏达大学）
We consider a distributed non-convex optimization where a network of agents aims at minimizing a global function over the Stiefel manifold. The global function is represented as a finite sum of smooth local functions, where each local function is associated with one agent and agents communicate with each other over an undirected connected graph. The problem is non-convex as local functions are possibly non-convex (but smooth) and the Steifel manifold is a non-convex set. We present a decentralized Riemannian stochastic gradient method (DRSGD) with the convergence rate of $\mathcal{O}(1/\sqrt{K…
This paper introduces an open-source software for distributed and decentralized non-convex optimization named ALADIN-$α$. ALADIN-$α$ is a MATLAB implementation of tailored variants of the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm. Its user interface is convenient for rapid prototyping of non-convex distributed optimization algorithms. An improved version of the recently proposed bi-level variant of ALADIN is included enabling decentralized non-convex optimization with reduced information exchange. A collection of examples from different applications fields in…
