Dynamics analysis and fractional-order nonlinearity system via memristor-based Chua oscillator

被引:5
作者
Sabarathinam, S. [1 ]
Papov, Viktor [1 ]
Wang, Zi-Peng [2 ,3 ,4 ]
Vadivel, R. [5 ]
Gunasekaran, Nallappan [6 ,7 ]
机构
[1] HSE Moscow, Natl Res Univ, Fac Comp Sci, Lab Complex Syst Modelling & Control, Moscow, Russia
[2] Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intelligen, Beijing 100124, Peoples R China
[3] Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China
[4] Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
[5] Phuket Rajabhat Univ, Fac Sci & Technol, Dept Math, Phuket 83000, Thailand
[6] Toyota Technol Inst, Computat Intelligence Lab, Nagoya 4688511, Japan
[7] Beibu Gulf Univ, Eastern Michigan Joint Coll Engn, Qinzhou 535011, Peoples R China
来源
PRAMANA-JOURNAL OF PHYSICS | 2023年 / 97卷 / 03期
关键词
Memristor emulator; fractional-order system; memristor; linear parameter varying model; linear matrix inequality; 5; 45; -a; Gg; Tp; GUARANTEED COST CONTROL; NEURAL-NETWORKS; SYNCHRONIZATION; STABILITY; CHAOS; STABILIZATION;
D O I
10.1007/s12043-023-02590-5
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
This article discusses the utilisation of a Chua oscillator with a memristor to produce chaos with minimal nonlinearity. The memristor, a device that changes its flux or charges over time, has its nonlinear strength altered fractionally to determine the lowest-order memristor nonlinearity for generating chaos. An experimental analog circuit in real-time has been constructed. A linear parameter varying (LPV) approach, incorporating a suitable Lyapunov functional (LK) method, has been introduced to find new sufficient conditions for the robust stability of the resulting closed-loop system through linear matrix inequalities (LMIs). By observing the behaviour of the system without control, it is possible to understand the basic characteristics of chaotic oscillations and how they are affected by changes in the fractional order. These results can then be used as a starting point to study the effectiveness of various control techniques, such as feedback control, in reducing chaos and stabilising the system of this article. The efficiency of the cost-function-based control scheme is evaluated using the simulation results and relevant applications are addressed.
引用
收藏
页数:13
相关论文
共 58 条
[1]  
Adamatzky A., 2013, Memristor networks
[2]   Fractional-order Wien-bridge oscillator [J].
Ahmad, W ;
El-khazali, R ;
Elwakil, AS .
ELECTRONICS LETTERS, 2001, 37 (18) :1110-1112
[3]   Chaos in fractional-order autonomous nonlinear systems [J].
Ahmad, WM ;
Sprott, JC .
CHAOS SOLITONS & FRACTALS, 2003, 16 (02) :339-351
[4]   Finite time decentralized event-triggered communication scheme for neutral-type Markovian jump neural networks with time varying delays [J].
Ali, Syed ;
Vadivel, R. ;
Murugan, Kadarkarai .
CHINESE JOURNAL OF PHYSICS, 2018, 56 (05) :2448-2464
[5]   Extended Dissipativity and Non-Fragile Synchronization for Recurrent Neural Networks With Multiple Time-Varying Delays via Sampled-Data Control [J].
Anbuvithya, R. ;
Sri, S. Dheepika ;
Vadivel, R. ;
Gunasekaran, Nallappan ;
Hammachukiattikul, Porpattama .
IEEE ACCESS, 2021, 9 :31454-31466
[6]  
Baleanu D., 2011, Fractional Dynamics and Control, DOI DOI 10.1007/978-1-4614-0457-6
[7]   Projective synchronization of fractional-order memristor-based neural networks [J].
Bao, Hai-Bo ;
Cao, Jin-De .
NEURAL NETWORKS, 2015, 63 :1-9
[8]   Anti-synchronization of delayed memristive neural networks with leakage term and reaction-diffusion terms [J].
Cao, Yanyi ;
Jiang, Wenjun ;
Wang, Jiahai .
KNOWLEDGE-BASED SYSTEMS, 2021, 233
[9]  
Chen L., 2021, OPTIM
[10]   Output-feedback-guaranteed cost control of fractional-order uncertain linear delayed systems [J].
Chen, Liping ;
Li, Tingting ;
Wu, Ranchao ;
Lopes, Antonio M. ;
Tenreiro Machado, J. A. ;
Wu, Kehan .
COMPUTATIONAL & APPLIED MATHEMATICS, 2020, 39 (03)