Event‐triggered output tracking control for wireless networked control systems with communication delays and data dropouts

Peng, Chen; Song, Yang; Xie, Xiang; Zhao, Min; Fei, Mei-Rui

doi:10.1049/iet-cta.2015.1314

Cited by 41 publications

(18 citation statements)

References 36 publications

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“…In the following, we will find a feedback gain G such that the system ( A + BG , C ) is resilient against attacks, and the system performance is improved under the constraints that the pair ( A , B ) is controllable (Duan et al, 2005) (Peng et al, 2016).…”

Section: Svsgd Algorithmmentioning

confidence: 99%

“…There are many diverse attack forms existing in CPS, for example, denial of service (DoS) attack (Zhang et al, 2016), date injection attack (Cui et al, 2012) and selective packet forward (Karlof et al, 2003), and so forth. These attacks impact seriously on the performance of the system, such as it gives a false service signal, replays data information or runs out of resources, and so on (Peng and Zhang, 2016; Peng et al, 2016). Thus, many scholars have done some valuable research for a system with sensor attacks.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive state estimation for cyber physical systems under sparse attacks

Zhang

Peng

Sun

et al. 2018

Transactions of the Institute of Measurement and Control

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This paper investigates the state estimation problem for cyber physical systems under sparse attacks. Firstly, the fundamental state estimation problem is transferred to an optimization problem with a unique solution. Secondly, an adaptive estimation method for sparse attacks is proposed, which convergence property is well proved. The advantage of proposed method is that the step-size can be adaptively adjusted based on the dynamic estimation errors. Therefore, the computing time is less than some existing methods while guaranteeing the desired performance. Then, a suitable state feedback is designed to improve the computing speed while enhancing the resiliency for the destroyed system. Finally, the speed performance and accuracy of proposed algorithm are verified by two numerical examples.

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Section: Svsgd Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive state estimation for cyber physical systems under sparse attacks

Zhang

Peng

Sun

et al. 2018

Transactions of the Institute of Measurement and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, event-triggered communication mechanism was born at the right moment [22,23]. As a popular research topic, some latest event-triggered control 2 Complexity results were provided in [24][25][26][27][28]. In particular, compared with the general event-triggered controller with a fixed threshold parameter, the authors in [24,29] developed the dynamic/adaptive event-triggered control protocol of multiagent systems for acquiring satisfactory resource efficiency, respectively.…”

Section: Introductionmentioning

confidence: 99%

Event‐Based Time Varying Formation Control for Multiple Quadrotor UAVs with Markovian Switching Topologies

Zhou

Wang

2018

Complexity

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Time varying formation control problem for a group of quadrotor unmanned aerial vehicles (UAVs) under Markovian switching topologies is investigated through a modified dynamic event-triggered control protocol. The formation shape is specified by a time varying vector, which prescribes the relative positions and bearings among the whole agents. Instead of the general stochastic topology, the graph is governed by a set of Markov chains to the edges, which can recover the traditional Markovian switching topologies in line with the practical communication network. The stability proof for the state space origin of the overall closed-loop system is derived from the singular perturbation method and Lyapunov stability theory. An event-triggered formation control protocol in terms of a dynamically varying threshold parameter is delicately carried out, while acquiring satisfactory resource efficiency, and Zeno behavior of triggering time sequences is excluded. Finally, simulations on six quadrotor UAVs are given to verify the effectiveness of the theoretical results.

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“…It is ensured that the states shrink into a bounded region around the origin in the presence of various kinds of quantisers. An event-based mechanism has been applied to networked systems in [12][13][14]. In this study, an eventdriven mechanism is introduced into the quantisation process so that the quantisation is performed at some instants rather than at every sampling instants, the purpose of which is to reduce the transmission frequency.…”

Section: Introductionmentioning

confidence: 99%

Dynamic sliding mode controller design for networked control systems with random packet loss and event driven quantisation

2018

IET Control Theory & Applications

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Event‐triggered output tracking control for wireless networked control systems with communication delays and data dropouts

Cited by 41 publications

References 36 publications

Adaptive state estimation for cyber physical systems under sparse attacks

Adaptive state estimation for cyber physical systems under sparse attacks

Event‐Based Time Varying Formation Control for Multiple Quadrotor UAVs with Markovian Switching Topologies

Dynamic sliding mode controller design for networked control systems with random packet loss and event driven quantisation

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