Switching event‐triggering mechanisms for integral input‐to‐state stable nonlinear systems

2021 ◽  
Author(s):  
Hao Yu ◽  
Xia Chen ◽  
Fei Hao
Keyword(s):  
Nonlinear Systems ◽  
Switching Event ◽  
Event Triggering ◽  
Triggering Mechanisms

scholarly journals Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1242
Author(s):  
Cong Huang ◽  
Bo Shen ◽  
Lei Zou ◽  
Yuxuan Shen
Keyword(s):  
Nonlinear Systems ◽  
Difference Equations ◽  
Estimation Error ◽  
Upper Bounds ◽  
The State ◽  
Fault Estimation ◽  
Triggering Mechanism ◽  
Transmission Frequency ◽  
Current Transmission ◽  
Event Triggering

This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds, relying on the solutions to a set of difference equations. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm.

scholarly journals Quantized-Feedback-Based Adaptive Event-Triggered Control of a Class of Uncertain Nonlinear Systems

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1603
Author(s):  
Yun Ho Choi ◽  
Sung Jin Yoo
Keyword(s):  
Nonlinear Systems ◽  
Total Sample ◽  
Control Law ◽  
Continuous Control ◽  
State Variables ◽  
Event Time ◽  
Quantized Feedback ◽  
Event Triggering ◽  
Sample Data ◽  
Event Triggered

A quantized-feedback-based adaptive event-triggered tracking problem is investigated for strict-feedback nonlinear systems with unknown nonlinearities and external disturbances. All state variables are quantized through a uniform quantizer and the quantized states are only measurable for the control design. An approximation-based adaptive event-triggered control strategy using quantized states is presented. Compared with the existing recursive quantized feedback control results, the primary contributions of the proposed strategy are (1) to derive a quantized-states-based function approximation mechanism for compensating for unknown and unmatched nonlinearities and (2) to design a quantized-states-based event triggering law for the intermittent update of the control signal. A Lyapunov-based stability analysis is provided to conclude that closed-loop signals are uniformly ultimately bounded and there exists a minimum inter-event time for excluding Zeno behavior. In simulation results, it is shown that the proposed quantized-feedback-based event-triggered control law can be implemented with less than 10% of the total sample data of the existing quantized-feedback continuous control law.

scholarly journals CAN network in real time on board systems

2017 ◽  
Vol 171 (4) ◽  
pp. 211-214
Author(s):  
Michał ŚMIEJA ◽  
Sławomir WIERZBICKI ◽  
Jarosław MAMALA
Keyword(s):  
Real Time ◽  
Data Transmission ◽  
Data Exchange ◽  
Data Transfer ◽  
Network Data ◽  
Network System ◽  
Exchange System ◽  
Use Of Time ◽  
Event Triggering ◽  
Triggering Mechanisms

The network system of data exchange between the various components is an inherent element of every car. Because of the specific and different requirements for data transfer between specific devices, currently used communication protocols have different properties of performance, security, and degree of determinism. The paper presents the increasing complexity of the data exchange system based on the example of the latest requirements for digital tachographs. The article describes also the data transmission initialization methods in the context of network data exchange organization. The hybrid use of time triggering and event triggering mechanisms has been presented in relation to the operation of the CAN network under increased real-time conditions requirements implemented as TTCAN (time triggered CAN).

Finite-time control of periodic systems with event-triggering mechanisms

2020 ◽  
Vol 14 (8) ◽  
pp. 1012-1021
Author(s):  
Liu Yang ◽  
Yabin Gao ◽  
Yuxin Zhao ◽  
Ligang Wu
Keyword(s):  
Finite Time ◽  
Periodic Systems ◽  
Time Control ◽  
Event Triggering ◽  
Triggering Mechanisms

Event-triggered particle filtering and Cramér–Rao lower bound computation

2020 ◽  
pp. 095965182094932
Author(s):  
Nargess Sadeghzadeh-Nokhodberiz ◽  
Mohammadreza Davoodi ◽  
Nader Meskin
Keyword(s):  
Nonlinear Systems ◽  
Lower Bound ◽  
Particle Filtering ◽  
Information Exchange ◽  
Minimum Mean Square Error ◽  
Stochastic Nonlinear Systems ◽  
Event Triggering ◽  
Estimation Scheme ◽  
Cramer Rao Lower Bound ◽  
Event Triggered

In this article, an event-triggered particle filtering method is presented to estimate the states of stochastic nonlinear systems with the ultimate goal to reduce the information exchange in networked systems. In the event-triggered estimation, measurements are transferred to an estimator only if certain event conditions are satisfied. Using these event-triggered measurements leads to non-Gaussianity of the conditional posterior distribution in minimum mean square error estimators even in the presence of Gaussian process and measurement noises. Therefore, in this article, a particle filter–based method is employed to solve the non-Gaussianity issue in nonlinear systems due to event-triggered measurements. In the proposed scheme, when no information is sent to the estimator, particles weight update role is modified according to the event-triggering probability density function. To evaluate the performance of the proposed state estimation scheme, the conditional posterior Cramér–Rao lower bound is obtained using Monte Carlo simulations. The bound is also computed for nonlinear Gaussian systems with a Gaussian event-triggering mechanism as a special case. Finally, the efficiency of the proposed method is demonstrated for a networked interconnected four-tank system through simulation and a comparison study is also provided.

scholarly journals Adaptive Event Triggering Normed Observer Control of Nonlinear Time Delayed Systems

2020 ◽  
Author(s):  
ayyob Asadbeigi
Keyword(s):  
Nonlinear Systems ◽  
Tracking Error ◽  
Adaptive Observer ◽  
Ultimate Boundedness ◽  
Delayed Systems ◽  
Event Triggering ◽  
Uniform Ultimate Boundedness ◽  
Error System ◽  
Event Based

In this paper, event-triggering state-norm estimators are studied for nonlinear delayed systems also normed-observability concept is extended for these systems. Moreover, we established a Lyapunov–Krasovskii functional to obtain normed observability of the delayed nonlinear systems. Furthermore, an adaptive observer is developed such that convergence of the error system is guaranteed. The proposed event-triggering algorithm yields an event-based observer that ensures uniform ultimate boundedness of the tracking error.

scholarly journals Decentralized Event-Triggering for Control of Nonlinear Systems

2014 ◽  
Vol 59 (12) ◽  
pp. 3312-3324 ◽  
Author(s):  
Pavankumar Tallapragada ◽  
Nikhil Chopra
Keyword(s):  
Nonlinear Systems ◽  
Event Triggering

Fuzzy model-based controller synthesis for networked nonlinear systems with event triggering communication scheme

2018 ◽  
Vol 50 (2) ◽  
pp. 379-391 ◽  
Author(s):  
Changzhu Zhang ◽  
Mengjiao Shen ◽  
Zhuping Wang ◽  
Chengju Liu ◽  
Jinfei Hu
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Model ◽  
Controller Synthesis ◽  
Model Based ◽  
Event Triggering ◽  
Communication Scheme

Periodic event-triggering in distributed receding horizon control of nonlinear systems

2015 ◽  
Vol 86 ◽  
pp. 16-23 ◽  
Author(s):  
Huiping Li ◽  
Weisheng Yan ◽  
Yang Shi ◽  
Yintao Wang
Keyword(s):  
Nonlinear Systems ◽  
Receding Horizon Control ◽  
Receding Horizon ◽  
Event Triggering
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