Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology

This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks

Bipartite consensus of nonlinear agents in the presence of communication noise

In this paper, a Distributed Nonlinear Dynamic Inversion (DNDI)-based consensus protocol is designed to achieve the bipartite consensus of nonlinear agents over a signed graph. DNDI inherits the advantage of nonlinear dynamic inversion theory, and the application to the bipartite problem is a new idea. Moreover, communication noise is considered to make the scenario more realistic. The convergence study provides a solid theoretical base, and a realistic simulation study shows the effectiveness of the proposed protocol.Engineering and Physical Sciences Research Council (EPSRC): EP/R009953/

Learning-based Robust Bipartite Consensus Control for a Class of Multiagent Systems

This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multi-agent systems (MASs) with fixed and switching topologies against data dropout and unknown disturbances. At first, the controlled system's virtual linear data model is developed by employing the pseudo partial derivative technique, and a distributed combined measurement error function is established utilizing a signed graph theory. Then, an input gain compensation scheme is formulated to mitigate the effects of data dropout in both feedback and forward channels. Moreover, a data-driven learning-based robust bipartite consensus control (LRBCC) scheme based on a radial basis function neural network observer is proposed to estimate the unknown disturbance, using the online input/output data without requiring any information on the mathematical dynamics. The stability analysis of the proposed LRBCC approach is given. Simulation and hardware testing also illustrate the correctness and effectiveness of the designed method

Consensus tracking of nonlinear agents using distributed nonlinear dynamic inversion with switching leader-follower connection

In this paper, a consensus tracking protocol for nonlinear agents is presented, which is based on the Nonlinear Dynamic Inversion (NDI) technique. Implementation of such a technique is new in the context of the consensus tracking problem. The tracking capability of nonlinear dynamic inversion (NDI) is exploited for a leader-follower multi-agent scenario. We have provided all the mathematical details to establish its theoretical foundation. Additionally, a convergence study is provided to show the efficiency of the proposed controller. The performance of the proposed controller is evaluated in the presence of both (a) random switching topology among the agents and (b) random switching of leader–follower connections, which is realistic and not reported in the literature. The follower agents track various trajectories generated by a dynamic leader, which describes the tracking capability of the proposed controller. The results obtained from the simulation study show how efficiently this controller can handle the switching topology and switching leader-follower connections.Engineering and Physical Sciences Research Council (EPSRC): EP/R009953/

Consensus of Multi-agent Reinforcement Learning Systems: The Effect of Immediate Rewards

This paper studies the consensus problem of a leaderless, homogeneous, multi-agent reinforcement learning (MARL) system using actor-critic algorithms with and without malicious agents. The goal of each agent is to reach the consensus position with the maximum cumulative reward. Although the reward function converges in both scenarios, in the absence of the malicious agent, the cumulative reward is higher than with the malicious agent present. We consider here various immediate reward functions. First, we study the immediate reward function based on Manhattan distance. In addition to proposing three different immediate reward functions based on Euclidean, $n$-norm, and Chebyshev distances, we have rigorously shown which method has a better performance based on a cumulative reward for each agent and the entire team of agents. Finally, we present a combination of various immediate reward functions that yields a higher cumulative reward for each agent and the team of agents. By increasing the agents’ cumulative reward using the combined immediate reward function, we have demonstrated that the cumulative team reward in the presence of a malicious agent is comparable with the cumulative team reward in the absence of the malicious agent. The claims have been proven theoretically, and the simulation confirms theoretical findings

Neuro-adaptive augmented distributed nonlinear dynamic inversion for consensus of nonlinear agents with unknown external disturbance

This paper presents a novel neuro-adaptive augmented distributed nonlinear dynamic inversion (N-DNDI) controller for consensus of nonlinear multi-agent systems in the presence of unknown external disturbance. N-DNDI is a blending of neural network and distributed nonlinear dynamic inversion (DNDI), a new consensus control technique that inherits the features of Nonlinear Dynamic Inversion (NDI) and is capable of handling the unknown external disturbance. The implementation of NDI based consensus control along with neural networks is unique in the context of multi-agent consensus. The mathematical details provided in this paper show the solid theoretical base, and simulation results prove the effectiveness of the proposed scheme.Engineering and Physical Sciences Research Council (EPSRC): EP/R009953/1