184 research outputs found
Distributed estimation over a low-cost sensor network: a review of state-of-the-art
Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted
On leaderless consensus of fractional-order nonlinear multi-agent systems via event-triggered control
The consensus problem of fractional-order multi-agent systems is investigated by eventtriggered control in this paper. Based on the graph theory and the Lyapunov functional approach, the conditions for guaranteeing the consensus are derived. Then, according to some basic theories of fractional-order differential equation and some properties of Mittag–Leffler function, the Zeno behavior could be excluded. Finally, a simulation example is given to check the effectiveness of the theoretical result
Optimized state estimation for nonlinear dynamical networks subject to fading measurements and stochastic coupling strength: An event-triggered communication mechanism
summary:This paper is concerned with the design of event-based state estimation algorithm for nonlinear complex networks with fading measurements and stochastic coupling strength. The event-based communication protocol is employed to save energy and enhance the network transmission efficiency, where the changeable event-triggered threshold is adopted to adjust the data transmission frequency. The phenomenon of fading measurements is described by a series of random variables obeying certain probability distribution. The aim of the paper is to propose a new recursive event-based state estimation strategy such that, for the admissible linearization error, fading measurements and stochastic coupling strength, a minimum upper bound of estimation error covariance is given by designing the estimator gain. Furthermore, the monotonicity relationship between the trace of the upper bound of estimation error covariance and the fading probability is pointed out from the theoretical aspect. Finally, a simulation example is used to show the effectiveness of developed state estimation algorithm
Event-Triggered Consensus and Formation Control in Multi-Agent Coordination
The focus of this thesis is to study distributed event-triggered
control for multi-agent systems (MASs) facing constraints in
practical applications. We consider several problems in the
field, ranging from event-triggered consensus with information
quantization, event-triggered edge agreement under
synchronized/unsynchronized clocks, event-triggered
leader-follower consensus with Euler-Lagrange agent dynamics and
cooperative event-triggered rigid formation control.
The first topic is named as event-triggered consensus with
quantized relative state measurements. In this topic, we develop
two event-triggered controllers with quantized relative state
measurements to achieve consensus for an undirected network where
each agent is modelled by single integrator dynamics. Both
uniform and logarithmic quantizers are considered, which,
together with two different controllers, yield four cases of
study in this topic. The quantized information is used to update
the control input as well as to determine the next trigger event.
We show that approximate consensus can be achieved by the
proposed algorithms and Zeno behaviour can be completely excluded
if constant offsets with some computable lower bounds are added
to the trigger conditions.
The second topic considers event-triggered edge agreement
problems. Two cases, namely the synchronized clock case and the
unsynchronized clock case, are studied. In the synchronized clock
case, all agents are activated simultaneously to measure the
relative state information over edge links under a global clock.
Edge events are defined and their occurrences trigger the update
of control inputs for the two agents sharing the link. We show
that average consensus can be achieved with our proposed
algorithm. In the unsynchronized clock case, each agent executes
control algorithms under its own clock which is not synchronized
with other agents' clocks. An edge event only triggers control
input update for an individual agent. It is shown that all agents
will reach consensus in a totally asynchronous manner.
In the third topic, we propose three different distributed
event-triggered control algorithms to achieve leader-follower
consensus for a network of Euler-Lagrange agents. We firstly
propose two model-independent algorithms for a subclass of
Euler-Lagrange agents without the vector of gravitational
potential forces. A variable-gain algorithm is employed when the
sensing graph is undirected; algorithm parameters are selected in
a fully distributed manner with much greater flexibility compared
to all previous work concerning event-triggered consensus
problems. When the sensing graph is directed, a constant-gain
algorithm is employed. The control gains must be centrally
designed to exceed several lower bounding inequalities which
require limited knowledge of bounds on the matrices describing
the agent dynamics, bounds on network topology information and
bounds on the initial conditions. When the Euler-Lagrange agents
have dynamics which include the vector of gravitational potential
forces, an adaptive algorithm is proposed. This requires more
information about the agent dynamics but allows for the
estimation of uncertain agent parameters.
The last topic discusses cooperative stabilization control of
rigid formations via an event-triggered approach. We first design
a centralized event-triggered formation control system, in which
a central event controller determines the next triggering time
and broadcasts the event signal to all the agents for control
input update. We then build on this approach to propose a
distributed event control strategy, in which each agent can use
its local event trigger and local information to update the
control input at its own event time. For both cases, the trigger
condition, event function and trigger behaviour are discussed in
detail, and the exponential convergence of the formation system
is guaranteed
Recommended from our members
Communication-protocol-based analysis and synthesis of networked systems: progress, prospects and challenges
In recent years, the communication-protocol-based synthesis and analysis issues have gained substantial research interest owing mainly to their significance in networked systems. In this work, we survey the control and filtering problems of networked systems under the effects induced by communication protocols. First, we introduce the engineering background of networked systems as well as the theoretical frameworks established to deal with the communication-protocol-based analysis and synthesis problems. Then, recent advances (especially the latest results) are reviewed on the stability analysis issue subject to protocol scheduling. Subsequently, the particular effort is devoted to presenting the latest progress on various communication-protocol-based control and filtering problems according to the characteristics of networked systems (e.g. time-varying nature, random behaviours, types of parameter uncertainties, and kinds of distributed structure). After that, we provide a systematic review of the communication-protocol-based fault diagnosis problems. Finally, some research challenges of communication-protocol-based control and filtering problems are outlined for future research
Simultaneous Estimation of Vehicle Sideslip and Roll Angles Using an Event-Triggered-Based IoT Architecture
In recent years, there has been a significant integration of advanced technology into the automotive industry, aimed primarily at enhancing safety and ride comfort. While a notable proportion of these driver-assist systems focuses on skid prevention, insufficient attention has been paid to addressing other crucial scenarios, such as rollovers. The accurate estimation of slip and roll angles plays a vital role in ensuring vehicle control and safety, making these parameters essential, especially with the rise of modern technologies that incorporate networked communication and distributed computing. Furthermore, there exists a lag in the transmission of information between the various vehicle systems, including sensors, actuators, and controllers. This paper outlines the design of an IoT architecture that accurately estimates the sideslip angle and roll angle of a vehicle, while addressing network transmission delays with a networked control system and an event-triggered communication scheme. Experimental results are presented to validate the performance of the IoT architecture proposed. The event-triggered scheme of the IoT solution is used to decrease data transmission and prevent network overload.Funding. Grant [ PID2022-136468OB-I00 ] funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”
Distributed control based on evolutionary game theory: multi-agent experiment
In this Master's thesis are applied distributed consensus and game theoretical algorithms to control a population of agents. The agents are embodies by Lego Mindstorm robots and controlled remotely with Bluetooth by the PC. Using a platform implemented in LabVIEW, which includes a camera and a pattern recognition tool, the robots are controlled to perform different tasks such as convergence to consensus position and formatio
Event-triggered filtering and fault estimation for nonlinear systems with stochastic sensor saturations
This paper is concerned with the filtering problem for a class of nonlinear systems with stochastic sensor saturations and event-triggered measurement transmissions. An event-triggered transmission scheme is proposed with hope to ease the traffic burden and improve the energy efficiency. The measurements are subject to randomly occurring sensor saturations governed by Bernoulli-distributed sequences. Special effort is made to obtain an upper bound of the filtering error covariance in the presence of linearisation errors, stochastic sensor saturations as well as event-triggered transmissions. A filter is designed to minimise the obtained upper bound at each time step by solving two sets of Riccati-like matrix equations, and thus the recursive algorithm is suitable for online computation. Sufficient conditions are established under which the filtering error is exponentially bounded in mean square. The applicability of the presented method is demonstrated by dealing with the fault estimation problem. An illustrative example is exploited to show the effectiveness of the proposed algorithm.This work was supported by the National Natural Science Foundation of China [grant number
61490701], [grant number 61522309], [grant number 61290324], [grant number 61473163], [grant
number 61273156], Research Fund for the Taishan Scholar Project of Shandong Province of China,
Tsinghua University Initiative Scientific Research Program, and Jiangsu Provincial Key Laboratory
of E-business at Nanjing University of Finance and Economics of China [grant number
JSEB201301]
- …