Consensus-based Distributed Estimation Research Articles

Multi-Agent Coordination of Thermostatically Controlled Loads by Smart Power Sockets for Electric Demand Side Management

This article presents a multi-agent control architecture and an online optimization method based on a dynamic average consensus to coordinate the power consumption of a large population of thermostatically controlled loads (TCLs). Our objective is to penalize peaks of power demand, smooth the load profile, and enable demand-side management. The proposed architecture and methods exploit only local measurements of power consumption via smart power sockets (SPSs) with no access to their internal temperature. No centralized aggregator of information is exploited, and agents preserve their privacy by cooperating anonymously only through consensus-based distributed estimation. The interactions among devices occur through an unstructured peer-to-peer (P2P) network over the Internet. Methods for parameter identification, state estimation, and mixed logical modeling of TCLs and SPSs are included. The architecture is designed from a multi-agent and plug-and-play perspective, in which existing household appliances can interact with each other in an urban environment. Finally, a novel low-cost testbed is proposed along with numerical tests and experimental validation.

Unreliable communication in high-performance distributed multi-agent systems: A ingenious scheme in high computing

Designing of distributed consensus algorithms featuring accuracy, robustness, reliability, and speed of convergence is in high demand for various multi-agent applications. In this research, it has been investigated to device a novel design of distributed estimation algorithm which can tackle the problem of unreliable communication among multi-agents to achieve consensus on the average value of their initial values and must be capable of computing the total number of agents in the system under dynamically changing interaction topologies. A dynamically changing network topology is considered in this research with unreliable communication links, and four different scenarios are established to be analyzed for the proposed consensus-based distributed estimation algorithm. This study established a consensus for a dynamically changing interaction topology among agents, for addition of agents in the network with dynamically switching topology at any instant in communication, for removal of agents from the network with dynamically switching topology at any instant in communication, and for a fixed topology with link failure and a reconnection with the same agent after each iteration. The proposed algorithm paces up the rate of convergence by reducing the number of iteration, along with sure convergence of the designed algorithm using the concepts of stochastic differential equation theory, control system theory, algebraic graph theory, and algebraic matrix theory. Finally, in the end, simulation results are provided which are clear evidence to validate the effectiveness of theoretical results of the proposed algorithm in comparison to previously known consensus algorithms in terms of different performance parameters.

Guest editorial: special issue on Consensus‐based Applications in Networked Systems

Guest editorial: special issue on Consensus‐based Applications in Networked Systems

Joint Input and State Estimation for Linear Discrete-Time Networked Systems

This paper proposes a distributed method for jointly estimating the input and the state of a given system observed through a sensor network. Traditionally, an unbiased state estimation can be obtained by using distributed Kalman filters if the system is subject to noise with known stochastic properties. Unfortunately, if the system is subject to a completely unknown input, representing faults, unknown disturbances or unmodeled dynamics, the estimated state is no longer unbiased. This study proposes new distributed filters that allow carrying out an unbiased state estimation in the presence of the unknown input. In addition, the proposed filters provide an estimation of the unknown inputs. Herein, it is assumed that the unknown input affects sensor measurements as well as system states.Two consensus-based distributed estimation algorithms are provided in this paper. The first algorithm gives an optimal minimum variance estimation if perfect consensus is achieved while the second algorithm provides a suboptimal solution with less computation effort.

Optimal consensus-based distributed estimation with intermittent communication

In this article, we consider the problem of distributed state estimation over a wireless sensor network (WSN). We firstly propose a distributed algorithm to estimate the state of a system by modelling the WSN as a directed network. Based on the Kalman filter, we introduce a consensus scheme for each sensor by including the estimates received from its neighbour sensors. We also consider intermittent and random data packet drops which are frequently seen in wireless networks. A sufficient condition is derived for the convergence of the state estimation error, and a upper bound is obtained for the estimation error covariance. We further consider minimising the estimation error by finding an optimal consensus gain for a given fixed network. The performance and effectiveness of the proposed algorithm are compared with existing well-known results from the literature.