This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Event-triggered consensus of multiagent systems (MASs) has attracted tremendous attention from both theoretical and practical perspectives due to the fact that it enables all agents eventually to reach an agreement upon a common quantity of interest while significantly alleviating utilization of communication and computation resources. This paper aims to provide an overview of recent advances in event-triggered consensus of MASs. First, a basic framework of multiagent event-triggered operational mechanisms is established. Second, representative results and methodologies reported in the literature are reviewed and some in-depth analysis is made on several event-triggered schemes, including event-based sampling schemes, model-based event-triggered schemes, sampled-data-based event-triggered schemes, and self-triggered sampling schemes. Third, two examples are outlined to show applicability of event-triggered consensus in power sharing of microgrids and formation control of multirobot systems, respectively. Finally, some challenging issues on event-triggered consensus are proposed for future research.

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An Overview of Recent Advances in Event-Triggered Consensus of Multiagent Systems

This paper provides an overview and makes a deep investigation on sampled-data-based event-triggered control and filtering for networked systems. Compared with some existing event-triggered and self-triggered schemes, a sampled-data-based event-triggered scheme can ensure a positive minimum inter-event time and make it possible to jointly design suitable feedback controllers and event-triggered threshold parameters. Thus, more attention has been paid to the sampled-data-based event-triggered scheme. A deep investigation is first made on the sampled-data-based event-triggered scheme. Then, recent results on sampled-data-based event-triggered state feedback control, dynamic output feedback control, $H_\infty$ filtering for networked systems are surveyed and analyzed. An overview on sampled-data-based event-triggered consensus for distributed multiagent systems is given. Finally, some challenging issues are addressed to direct the future research.

An Overview and Deep Investigation on Sampled-Data-Based Event-Triggered Control and Filtering for Networked Systems

This paper addresses the distributed formation control problem of a networked multi-agent system (MAS) subject to limited communication resources. First, a dynamic event-triggered communication mechanism (DECM) is developed to schedule inter-agent communication such that some unnecessary data exchanges among agents can be reduced so as to achieve better resource efficiency. Different from most of the existing event-triggered communication mechanisms, wherein threshold parameters are fixed all the time, the threshold parameter in the developed event triggering condition is dynamically adjustable in accordance with a dynamic rule. It is numerically shown that the proposed DECM can achieve a better tradeoff between reducing inter-agent communication frequency and preserving an expected formation than some existing ones. Second, an event-triggered formation protocol is delicately proposed by using only locally triggered sampled data in a distributed manner. Based on the formation protocol, it is shown that the state formation control problem is cast into an asymptotic stability problem of a reduced-order closed-loop system. Then, criteria for designing desired formation protocol and communication mechanism are derived. Finally, the effectiveness and advantages of the proposed approach are demonstrated through a comparative study in multirobot formation control.

Distributed Formation Control of Networked Multi-Agent Systems Using a Dynamic Event-Triggered Communication Mechanism

The security problem of cyber-physical systems (CPSs) under the injection attack is investigated in this article. A switching data injection attack strategy based on the game approach is proposed, which makes the attacker consume less energy and more difficult to defend. In addition, the constraint of the limited attack resources is considered. Noting that the attacker and the defender are antagonistic in this article, the framework of a zero-sum game is used to describe this relation, and a finite-horizon quadratic cost function is also defined. The objective of this article is to find an appropriate offensive and defensive strategy to optimize this function. Different from the existing games, the upper bound on the energy consumption of each attack is considered. Pontryagin's maximum principle and saturation equivalent approach are used to solve this game problem. Thus, the optimal offensive and defensive strategy can be derived. Finally, a numerical simulation and a physical experiment with the wheeled mobile robot (WMR) validate the effectiveness of the proposed scheme.

Optimal Injection Attack Strategy for Cyber-Physical Systems Under Resource Constraint: A Game Approach

This letter studies the consensus problem of general linear multi-agent systems (GLMASs) with limitations of network bandwidth, by developing a novel distributed dynamic event-triggered protocol. First, by adopting an estimation algorithm and introducing an inner self-learning term into triggering conditions, a new distributed dynamic event-triggered protocol (DDETP) is proposed to schedule communications among adjacent agents. Then, based on the local state information, a distributed dynamic event-triggered protocol is developed, which can drive the GLMASs to reach exponential consensus and exclude the possible Zeno phenomena. It is proven that the developed DDETP reduces the number of information transmission adaptively, which means that less network bandwidth is required, with comparison to the existing consensus protocols. Finally, the effectiveness of the proposed DDETP is verified through a numerical simulation.

An Estimator-Based Dynamic Event-Triggered Protocol for Linear Multiagent Systems

This article is devoted to dealing with exponential synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) under the framework of aperiodic sampling and state quantization. First, by taking the effect of aperiodic sampling and state quantization into consideration, a novel quantized sampled-data (QSD) controller with time-varying control gain is designed to tackle the exponential synchronization of INNs. Second, considering the available information of the lower and upper bounds of each HTVD, a refined Lyapunov-Krasovskii functional (LKF) is proposed. Meanwhile, an improved looped-functional method is utilized to fully capture the characteristic of practical sampling patterns and further relax the positive definiteness requirement for LKF. Consequently, less conservative exponential synchronization conditions with extra flexibility are derived. Finally, a numerical example is employed to demonstrate the effectiveness and advantages of the proposed synchronization method.

Sampled-Data Control for Exponential Synchronization of Delayed Inertial Neural Networks With Aperiodic Sampling and State Quantization.

The distributed H ∞ state estimation problem over a filtering network with Markov switching topology is studied in this paper by employing event-triggered strategy. The strategy at each node is built on the output estimation error of its own and those received from its neighbours. Based on the communication uncertainty of practical networks, switching topology which subjects to a heterogeneous Markov chain is considered in filter design. By utilizing stochastic Markov stability theory, switching topology-dependent filters are designed such that the underlying error system is stochastically stable in mean square and the disturbance rejection attenuation level guarantees an H ∞ performance bound. An illustrative example is presented to show the applicability of the obtained results.

Distributed event triggered H ∞ filtering with nonhomogeneous Markov switching topologies

This paper studies the consensus problem of general linear multi-agent systems via self-triggered control. Two dis- tributed self-triggered control schemes based on state feedback and output feedback are developed respectively. It is shown that under the proposed control protocols, consensus can be reached if the communication graph of the multi-agent system is connected. An example is presented to illustrate the effectiveness of the proposed control methods. Index Terms—Multi-agent system (MAS), consensus, self- triggered control, state feedback, output feedback.

Huaicheng Yan

Papers

Optimal Injection Attack Strategy for Cyber-Physical Systems Under Resource Constraint: A Game Approach

An Estimator-Based Dynamic Event-Triggered Protocol for Linear Multiagent Systems

Sampled-Data Control for Exponential Synchronization of Delayed Inertial Neural Networks With Aperiodic Sampling and State Quantization.

Distributed event triggered H ∞ filtering with nonhomogeneous Markov switching topologies

Distributed Self-triggered Control for Consensus of