Adaptive event-triggered distributed recursive filtering with stochastic parameters and faults

2021 ◽  
pp. 014233122110379
Author(s):  
Lingling Wu ◽  
Derui Ding ◽  
Yamei Ju ◽  
Xiaojian Yi
Keyword(s):  
Upper Bound ◽  
Basis Matrix ◽  
Dynamic Changes ◽  
Recursive Filtering ◽  
Error Covariance ◽  
Stochastic Parameter ◽  
Stochastic Parameters ◽  
System States ◽  
Distributed Filter ◽  
Event Triggered

This paper investigates the distributed recursive filtering issue of a class of stochastic parameter systems with randomly occurring faults. An event-triggered scheme with an adaptive threshold is designed to better reduce the communication load by considering dynamic changes of measurement sequences. In the framework of Kalman filtering, a distributed filter is constructed to simultaneously estimate both system states and faults. Then, the upper bound of filtering error covariance is derived with the help of stochastic analysis combined with basis matrix inequalities. The obtained condition with a recursive feature is dependent on the statistical characteristic of stochastic parameter matrices as well as the time-varying threshold. Furthermore, the desired filter gain is derived by minimizing the trace of the obtained upper bound. Finally, two simulation examples are conducted to demonstrate the effectiveness and feasibility of the proposed filtering method.

Event-triggered based distributed H∞ consensus filtering for discrete-time delayed systems over lossy sensor network

2017 ◽  
Vol 40 (9) ◽  
pp. 2740-2747 ◽  
Author(s):  
Shenquan Wang ◽  
Yuenan Wang ◽  
Yulian Jiang ◽  
Yuanchun Li
Keyword(s):  
Discrete Time ◽  
Delay Systems ◽  
Linear Matrix ◽  
Output Measurement ◽  
Delayed Systems ◽  
Time Varying Delay ◽  
System States ◽  
The Individual ◽  
Distributed Filter ◽  
Event Triggered

This paper investigates the issue of event-triggered distributed H∞ consensus filtering for discrete time-varying delay systems over lossy sensor networks with stochastic switching topologies. For each sensor node, the event-triggering mechanism is given by an event detector, which determines whether to transmit the output measurement or not. The communication links between the event detector and the distributed filter are assumed to be over a lossy network, and the missing probability is governed by a set of random variables. Through available output measurements from not only the individual sensor but also its neighbouring sensors, according to the interconnection topology to estimate the system states, a sufficient condition is established for the desired distributed filter to ensure that the overall filtering dynamics are stochastically stable and achieve a prescribed distributed H∞ average performance. Meanwhile, the corresponding solvability conditions for the desired distributed filter gains are characterized in terms of feasibility linear matrix inequalities. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed approaches.

Event-triggered optimal Kalman Consensus Filter with Upper Bound of Error Covariance

2021 ◽  
pp. 108175
Author(s):  
Zeming Li ◽  
Yonggui Liu ◽  
Xiaoqing Hu ◽  
Wenfeng Dai
Keyword(s):  
Upper Bound ◽  
Error Covariance ◽  
Consensus Filter ◽  
Event Triggered

scholarly journals An Optimized Triggering Algorithm for Event-Triggered Control of Networked Control Systems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1262
Author(s):  
Sunil Kumar Mishra ◽  
Amitkumar V. Jha ◽  
Vijay Kumar Verma ◽  
Bhargav Appasani ◽  
Almoataz Y. Abdelaziz ◽  
...  
Keyword(s):  
Control Systems ◽  
Networked Control Systems ◽  
Networked Control ◽  
Pendulum System ◽  
Uniform Sampling ◽  
Inverted Pendulum System ◽  
Event Triggering ◽  
Non Uniform Sampling ◽  
System States ◽  
Event Triggered

This paper presents an optimized algorithm for event-triggered control (ETC) of networked control systems (NCS). Initially, the traditional backstepping controller is designed for a generalized nonlinear plant in strict-feedback form that is subsequently extended to the ETC. In the NCS, the controller and the plant communicate with each other using a communication network. In order to minimize the bandwidth required, the number of samples to be sent over the communication channel should be reduced. This can be achieved using the non-uniform sampling of data. However, the implementation of non-uniform sampling without a proper event triggering rule might lead the closed-loop system towards instability. Therefore, an optimized event triggering algorithm has been designed such that the system states are always forced to remain in stable trajectory. Additionally, the effect of ETC on the stability of backstepping control has been analyzed using the Lyapunov stability theory. Two case studies on an inverted pendulum system and single-link robot system have been carried out to demonstrate the effectiveness of the proposed ETC in terms of system states, control effort and inter-event execution time.

Zeno-free adaptive event-triggered control design

2018 ◽  
Vol 41 (8) ◽  
pp. 2328-2337 ◽  
Author(s):  
Hassan Adloo ◽  
Mohammad Hossein Shafiei
Keyword(s):  
Degrees Of Freedom ◽  
Linear Time ◽  
Sufficient Conditions ◽  
Performance Criterion ◽  
Linear Time Invariant ◽  
Event Triggering ◽  
Linear Time Invariant Systems ◽  
System States ◽  
Time Invariant ◽  
Event Triggered

This paper presents a new general framework for adaptive event-triggered control strategy to extend average inter-event interval, while maintaining the performance of the system. The proposed event-triggering mechanism is acquired from input to state stability conditions, which is defined in terms of system states as well as an adaptation parameter. Under the Lipschitz assumption, a positive lower bound on sampling durations is also established that is essential to restrain the Zeno behavior. Applying the proposed method to linear time-invariant systems, leads to sufficient conditions to guarantee asymptotic stability in the form of matrix inequalities. Moreover, it is shown that there exist more degrees of freedom to improve the performance criterion from theoretical aspects. Finally, in order to show capability of the proposed method and its better performance compared with some recent works, numerical simulations are presented.

An Optimal Consensus Control for Multiple Agents System with Time-Delay and Disturbances

2014 ◽  
Vol 490-491 ◽  
pp. 828-831 ◽  
Author(s):  
Dong Hao Wang ◽  
Jian Yuan ◽  
Juan Xu ◽  
Zhong Hai Zhou
Keyword(s):  
Time Delay ◽  
Transformation Method ◽  
Consensus Control ◽  
Recursive Filtering ◽  
Control Time ◽  
Disturbance Rejection Control ◽  
System States ◽  
Feedback Optimal Control ◽  
Persistent Disturbances ◽  
System With Time Delay

The optimal disturbance rejection control problem is considered for a kind of consensus with control time-delay affected by external persistent disturbances and noise. An transformation method is used to convert the consensus with control time-delay to the consensus system without time-delay. The optimal estimated values of the converted consensus system states are obtained by recursive filtering with Kalman filter. Then the feedforward-feedback optimal control law is deduced by solving the Riccati equations and matrix equations. Lastly, simulations show the result is effectiveness to the consensus system with time-delay with respect to external persistent disturbances and noise.

Robust State Estimation for Linear Systems

1993 ◽  
Vol 115 (1) ◽  
pp. 193-196
Author(s):  
S. S. Garimella ◽  
K. Srinivasan
Keyword(s):  
Dynamic System ◽  
State Estimation ◽  
Real Time ◽  
Linear Systems ◽  
Upper Bound ◽  
Estimation Error ◽  
System Model ◽  
Linear Dynamic ◽  
Modeling Errors ◽  
System States

Real-time state estimation of a linear dynamic system using an observer, in the presence of modeling errors in the system model used by the observer and uncertainty in the initial system states, is considered here. A guideline for designing observers for multioutput systems is established, based on an expression for an upper bound on the norm of the state estimation error derived in this paper. An example is presented to illustrate the usefulness of this guideline.

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