Synchronizing non-identical time-varying delayed neural network systems via iterative learning control

Qiang, Hao; Lin, Zongzong; Zou, Xiaoguang; Sun, Changkai; Lu, Wenlian

doi:10.1016/j.neucom.2020.05.053

Cited by 12 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Reference [139], authors investigated about the switching-like event-triggered control for networked control systems (NCSs) under the malicious denial of service (DoS) attacks. In Reference [140] authors proposed an iterative learning control (ILC) scheme to synchro-nize an array of non-identical neural network systems based on time-varying delay in a repetitive environment. In Reference [141], authors considered a resilient event-triggered control problem for a class of networked systems subject to randomly occurring deception attacks.…”

Section: Development Of Ncs From 2011 Onwardsmentioning

confidence: 99%

A Comprehensive Review of the Evolution of Networked Control System Technology and Its Future Potentials

et al. 2021

View full text Add to dashboard Cite

Networked control systems (NCSs) are attracting the attention of control system engineers. The NCS has created a paradigm shift in control system technology. An NCS consists of control loops joined through communication networks in which both the control signal and the feedback signal are exchanged between the system and the controller. However, its materialization faces several challenges as it requires the integration of advanced control and communication techniques. This paper presents an extensive review of NCSs from the perspective of control system design. The evolution of NCSs is broadly divided in three phases, namely NCSs prior to 2000, NCSs during 2001–2010, and NCSs from 2011 onwards. This division corresponds to the initial status, intermediate status, and the recent status of the developments in the design of NCSs. The advancement of different control techniques during these phases has been discussed comprehensively. This paper also describes the transition of control systems form continuous domain to networked domain, which makes it better than the traditional control systems. Some important practical applications, which have been implemented using NCSs, have also been discussed. The thrust areas for future research on NCS have also been identified.

show abstract

Section: Development Of Ncs From 2011 Onwardsmentioning

confidence: 99%

A Comprehensive Review of the Evolution of Networked Control System Technology and Its Future Potentials

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The adaptive iterative learning control method can solve the finite-time tracking control problem of uncertain non-linear systems [28] by modifying the input information according to the previous output data. This ensures that the output of the system tracks the given trajectory on a finite time interval accurately.…”

Section: Introductionmentioning

confidence: 99%

A new adaptive iterative learning control of finite-time hybrid function projective synchronization for unknown time-varying chaotic systems

et al. 2023

View full text Add to dashboard Cite

A new adaptive iterative learning control (AILC) scheme is proposed to solve the finite-time hybrid function projective synchronization (HFPS) problem of chaotic systems with unknown periodic time-varying parameters. Fourier series expansion (FSE) is introduced to deal with the problem of uncertain time-varying parameters. The bound of the expanded remaining items is unknown. A typical convergent series is used to deal with the unknown bound in the design process of the controller. The adaptive iterative learning synchronization controller and parameter update laws are designed. Two different chaotic systems are synchronized asymptotically according to different proportional functions on a finite time interval by Lyapunov stability analysis. The simulation example proves the feasibility and effectiveness of the proposed method.

show abstract

“…This is also supported by performing numerical examples of given hyperbolic systems. In addition, [23] shows the realization of the synchronization of non-identical neural network systems that have a variable delay in time coupled by means of an iterative learning control. According to the simulation results shown in [23], the synchronization objective is satisfied.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, [23] shows the realization of the synchronization of non-identical neural network systems that have a variable delay in time coupled by means of an iterative learning control. According to the simulation results shown in [23], the synchronization objective is satisfied. Finally, in [24], iterative learning control is applied to a novel computational fluid dynamics model to show the performance of the controller in improving the aerodynamic load of wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems

Acho

Pujol

2021

Sensors

View full text Add to dashboard Cite

In this article, we propose a recent iterative learning algorithm for sensor data fusion to detect pitch actuator failures in wind turbines. The development of this proposed approach is based on iterative learning control and Lyapunov’s theories. Numerical experiments were carried out to support our main contribution. These experiments consist of using a well-known wind turbine hydraulic pitch actuator model with some common faults, such as high oil content in the air, hydraulic leaks, and pump wear.

show abstract

Synchronizing non-identical time-varying delayed neural network systems via iterative learning control

Cited by 12 publications

References 32 publications

A Comprehensive Review of the Evolution of Networked Control System Technology and Its Future Potentials

A Comprehensive Review of the Evolution of Networked Control System Technology and Its Future Potentials

A new adaptive iterative learning control of finite-time hybrid function projective synchronization for unknown time-varying chaotic systems

Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems

Contact Info

Product

Resources

About