235 research outputs found
Performance analysis with network-enhanced complexities: On fading measurements, event-triggered mechanisms, and cyber attacks
Copyright © 2014 Derui Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Nowadays, the real-world systems are usually subject to various complexities such as parameter uncertainties, time-delays, and nonlinear disturbances. For networked systems, especially large-scale systems such as multiagent systems and systems over sensor networks, the complexities are inevitably enhanced in terms of their degrees or intensities because of the usage of the communication networks. Therefore, it would be interesting to (1) examine how this kind of network-enhanced complexities affects the control or filtering performance; and (2) develop some suitable approaches for controller/filter design problems. In this paper, we aim to survey some recent advances on the performance analysis and synthesis with three sorts of fashionable network-enhanced complexities, namely, fading measurements, event-triggered mechanisms, and attack behaviors of adversaries. First, these three kinds of complexities are introduced in detail according to their engineering backgrounds, dynamical characteristic, and modelling techniques. Then, the developments of the performance analysis and synthesis issues for various networked systems are systematically reviewed. Furthermore, some challenges are illustrated by using a thorough literature review and some possible future research directions are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 61203139, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Deep Reinforcement Learning for Swarm Systems
Recently, deep reinforcement learning (RL) methods have been applied
successfully to multi-agent scenarios. Typically, these methods rely on a
concatenation of agent states to represent the information content required for
decentralized decision making. However, concatenation scales poorly to swarm
systems with a large number of homogeneous agents as it does not exploit the
fundamental properties inherent to these systems: (i) the agents in the swarm
are interchangeable and (ii) the exact number of agents in the swarm is
irrelevant. Therefore, we propose a new state representation for deep
multi-agent RL based on mean embeddings of distributions. We treat the agents
as samples of a distribution and use the empirical mean embedding as input for
a decentralized policy. We define different feature spaces of the mean
embedding using histograms, radial basis functions and a neural network learned
end-to-end. We evaluate the representation on two well known problems from the
swarm literature (rendezvous and pursuit evasion), in a globally and locally
observable setup. For the local setup we furthermore introduce simple
communication protocols. Of all approaches, the mean embedding representation
using neural network features enables the richest information exchange between
neighboring agents facilitating the development of more complex collective
strategies.Comment: 31 pages, 12 figures, version 3 (published in JMLR Volume 20
Distributed Control of Multi-agent Systems with Unknown Time-varying Gains: A Novel Indirect Framework for Prescribed Performance
In this paper, a new yet indirect performance guaranteed framework is
established to address the distributed tracking control problem for networked
uncertain nonlinear strict-feedback systems with unknown time-varying gains
under a directed interaction topology. The proposed framework involves two
steps: In the first one, a fully distributed robust filter is constructed to
estimate the desired trajectory for each agent with guaranteed observation
performance that allows the directions among the agents to be non-identical. In
the second one, by establishing a novel lemma regarding Nussbaum function, a
new adaptive control protocol is developed for each agent based on backstepping
technique, which not only steers the output to asymptotically track the
corresponding estimated signal with arbitrarily prescribed transient
performance, but also largely extends the scope of application since the
unknown control gains are allowed to be time-varying and even state-dependent.
In such an indirect way, the underlying problem is tackled with the output
tracking error converging into an arbitrarily pre-assigned residual set
exhibiting an arbitrarily pre-defined convergence rate. Besides, all the
internal signals are ensured to be semi-globally ultimately uniformly bounded
(SGUUB). Finally, simulation results are provided to illustrate the
effectiveness of the co-designed scheme
Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey
This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances
on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301,
61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform
Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work based on hardware and software multi-agent systems. To achieve a high rate of landmine detection, we evaluate the performance of a trained system over the distribution of samples between training and validation sets. Additionally, a general explanation of the data set is provided, presenting the samples gathered by a cooperative multi-agent system developed for detecting improvised explosive devices. The results show that input samples affect the performance of the output decisions, and a decision-making system can be less sensitive to sensor noise with intelligent systems obtained from a diverse and suitably organised training set
Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems
Adaptive fuzzy control strategies are established to achieve global
prescribed performance with prescribed-time convergence for strict-feedback
systems with mismatched uncertainties and unknown nonlinearities. Firstly, to
quantify the transient and steady performance constraints of the tracking
error, a class of prescribed-time prescribed performance functions are
designed, and a novel error transformation function is introduced to remove the
initial value constraints and solve the singularity problem in existing works.
Secondly, based on dynamic surface control methods, controllers with or without
approximating structures are established to guarantee that the tracking error
achieves prescribed transient performance and converges into a prescribed
bounded set within prescribed time. In particular, the settling time and
initial value of the prescribed performance function are completely independent
of initial conditions of the tracking error and system parameters, which
improves existing results. Moreover, with a novel Lyapunov-like energy
function, not only the differential explosion problem frequently occurring in
backstepping techniques is solved, but the drawback of the semi-global
boundedness of tracking error induced by dynamic surface control can be
overcome. The validity and effectiveness of the main results are verified by
numerical simulations on practical examples
Finite-time and fixed-time sliding mode control for second-order nonlinear multiagent systems with external disturbances
In this paper, the leader-following consensus of second-order nonlinear multiagent systems (SONMASs) with external disturbances is studied. Firstly, based on terminal sliding model control method, a distributed control protocol is proposed over undirected networks, which can not only suppress the external disturbances, but also make the SONMASs achieve consensus in finite time. Secondly, to make the settling time independent of the initial values of systems, we improve the protocol and ensure that the SONMASs can reach the sliding surface and achieve consensus in fixed time if the control parameters satisfy some conditions. Moreover, for general directed networks, we design a new fixed-time control protocol and prove that both the sliding mode surface and consensus for SONMASs can be reached in fixed time. Finally, several numerical simulations are given to show the effectiveness of the proposed protocols
MARS: An Educational Environment for Multiagent Robot Simulations
Undergraduate robotics students often find it difficult to design and validate control algorithms for teams of mobile robots. This is mainly due to two reasons. First, very rarely educational laboratories are equipped with large teams of robots, which are usually expensive, bulky and difficult to manage and maintain. Second, robotics simulators often require student to spend much time to learn their use and functionalities.
For this purpose, a simulator of multi-agent mobile robots named MARS has been developed within the Matlab environment, with the aim of facilitating students to simulate a wide variety of control algorithms in an easy way and without spending time for understanding a new language. Through this facility, the user is able to simulate multi-robot teams performing different tasks, from cooperative to competitive ones, by using both centralized and distributed controllers. Virtual sensors are provided to simulate real devices. A graphical user interface allows students to monitor the robots behaviour through an online animation
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