Fuzzy neural network-based chaos synchronization for a class of fractional-order chaotic systems: an adaptive sliding mode control approach

Wang, Renming; Zhang, Yunning; Chen, YangQuan; Chen, Xi; Xi, Lei

doi:10.1007/s11071-020-05574-x

Cited by 64 publications

(42 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the SMC technique [40], [41], [42] and sampleddata feedback control approach [43], [44], [45], a hybrid or mixed SMC is designed to desire the GMLS for FNNTVD as follows…”

Section: Gmls Of Fnntvd Based On Hybrid Sliding Mode Controllermentioning

confidence: 99%

“…Compared with the controllers in [30], [31], [32], [33], [34], [35], [36], [37], SMC can deal with uncertainties and external disturbances more effectively [39]. Thereby, many scholars adopt SMC to study the dynamic performance of linear and nonlinear system [40], [41], [42]. For instance, in [40], a non-fragile observer-based adaptive SMC is designed to achieve stability for fractionalorder Markovian jump systems with time delay and input nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, fixed-time synchronization problem of fractional-order memristive MAM neural networks is discussed by using SMC in [41]. In [42], adaptive SMC for a class of fractional-order chaotic systems is proposed to ensure the fuzzy neural network-based chaos synchronization.…”

Section: Introductionmentioning

confidence: 99%

“…From the previous works [40], [41], [42], two important aspects of SMC have not been considered yet, which are the influence of input time delay and how to reduce overconsumption of network resources. In practical applications, especially when communication speed and channels are limited, the input time delay is an unavoidable phenomenon, which may affect the input control performance, leading to the local oscillation or even the instability of whole system.…”

Section: Introductionmentioning

confidence: 99%

“…In practical applications, especially when communication speed and channels are limited, the input time delay is an unavoidable phenomenon, which may affect the input control performance, leading to the local oscillation or even the instability of whole system. In addition, in the previous works [40], [41], [42], since all the control schemes are the continuous-time feedbacks, they easily lead to the over-consumption system's resources and the channel congestion. In order to avoid these problems, recently sampled-data control, as a classical the discrete-time feedback control, is introduced [43], [44], [45].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Global Mittag-Leffler Synchronization of Fractional-Order Neural Networks With Time-Varying Delay via Hybrid Sliding Mode Control

Yang

Chen

2020

IEEE Access

View full text Add to dashboard Cite

In this paper, the global Mittag-Leffler synchronization (GMLS) for fractional-order neural networks with time-varying delay (FNNTVD) is studied based on hybrid sliding mode control (HSMC). First, a class of sliding surface is designed to study the dynamic behavior of FNNTVD by using the integer order Lyapunov direct method, which resolves the problem that the activation function of FNNTVD cannot be applied to the construction of Lyapunov function in the previous literature. Then, a new HSMC with the fixed signal transmission time delay is developed to ensure GMLS of FNNTVD. Next, based on Lyapunov direct method and the designed HSMC, some criteria are put forward to achieve GMLS of FNNTVD. Finally, two examples are given to verify the effectiveness of the proposed synchronization criteria for FNNTVD.

show abstract

“…Based on the SMC technique [40], [41], [42] and sampleddata feedback control approach [43], [44], [45], a hybrid or mixed SMC is designed to desire the GMLS for FNNTVD as follows…”

Section: Gmls Of Fnntvd Based On Hybrid Sliding Mode Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Global Mittag-Leffler Synchronization of Fractional-Order Neural Networks With Time-Varying Delay via Hybrid Sliding Mode Control

Yang

Chen

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

Adaptive Robust Control of Nonlinear Constrained Stirred‐Tank Reactors via Self‐Organizing Fuzzy Neural Network

Cui,

Li,

2024

Adaptive Control & Signal

View full text Add to dashboard Cite

This paper presents an adaptive robust constraint controller for a continuous stirred tank reactor (CSTR) system based on a self‐organizing fuzzy neural network (SOFNN). Due to the high complexity of the chemical reactions, the CSTR system contains many strong nonlinearities and uncertainties. This is the first time to introduce the SOFNN into the adaptive controller design of the CSTR system, which improves the adaptability to dynamic system changes through the adjustment of the fuzzy network structure. Meanwhile, the time‐varying integral barrier Lyapunov functions (TVIBLFs) are employed to ensure the dimensionless reactant concentration and mixture temperature within a reasonable scope, which can improve the stability and safety of the CSTR system. Based on Lyapunov stability analysis, all the signals in a closed‐loop system are ultimately bounded. Simulation results substantiate the efficacy of the proposed control scheme.

show abstract

Adaptive barrier function‐based fractional‐order chattering‐free finite‐time control for uncertain chaotic systems

Sepestanaki

Soofi

Barhaghtalab

et al. 2023

Math Methods in App Sciences

View full text Add to dashboard Cite

This study proposes the adaptive continuous barrier function as the fractional‐order control system using the terminal sliding mode control technique with chattering‐free property to stabilize chaotic systems that have unknown uncertainties. The important reason for using the fractional‐order controller is its greater flexibility than the integer‐order controller. Using adaptive approach and Lyapunov's stability theory, an adaptive continuous barrier fractional‐order chattering‐free finite‐time controller for a category of chaotic systems with the unknown uncertainties and external disturbances is presented. The suggested controller can stabilize the chaotic system excellently with a continuous and smooth control law even without knowing the boundaries and in the presence of unknown disturbances due to the model uncertainties. According to MATLAB simulation results, the high efficiency of the suggested control technique to control the chaotic systems in the attendance of unknown perturbations is obviously confirmed.

show abstract

Fuzzy neural network-based chaos synchronization for a class of fractional-order chaotic systems: an adaptive sliding mode control approach

Cited by 64 publications

References 31 publications

Global Mittag-Leffler Synchronization of Fractional-Order Neural Networks With Time-Varying Delay via Hybrid Sliding Mode Control

Global Mittag-Leffler Synchronization of Fractional-Order Neural Networks With Time-Varying Delay via Hybrid Sliding Mode Control

Adaptive Robust Control of Nonlinear Constrained Stirred‐Tank Reactors via Self‐Organizing Fuzzy Neural Network

Adaptive barrier function‐based fractional‐order chattering‐free finite‐time control for uncertain chaotic systems

Contact Info

Product

Resources

About