A radial basis function sliding mode controller for chaotic Lorenz system

Guo, Huijun; Sui-fang, Lin; Liu, Junhua

doi:10.1016/j.physleta.2005.10.101

Cited by 37 publications

(7 citation statements)

References 10 publications

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“…( ) (5) which is in the form of back-stepping method, so the control law 2 u is as follows: (6) where 2 0 k > . This control law asymptotically stabilizes 12 (, )(0,0) xx = and Lyapunov function is as (7).…”

Section: Back-stepping Methodsmentioning

confidence: 99%

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Suppression of Chaotic Behavior in Duffing-holmes System using Back-stepping Controller Optimized by Unified Particle Swarm Optimization Algorithm

Gholipour

Khosravi

Mojallali³

2013

IJE

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The nonlinear behavior analysis and chaos control for Duffing-Holmes chaotic system is discussed in this paper. In order to suppress the irregular chaotic motion, an optimal back-stepping controller is designed. The back-stepping method consists of parameters with positive values. The improper selection of the parameters leads to inappropriate responses or even may lead to instability of the system. In this paper, the Unified particle swarm optimization (UPSO) algorithm is utilized to determine the convenient and optimal values of the parameters. The minimized objective function via UPSO algorithm is a weighted sum of the Integral of Time multiplied Absolute Error (ITAE) and squared control signal. Fast control of chaos in a very short time and having more limited control signal for this purpose, are the great advantages of the proposed controller. Numerical simulations show the high performance of this method for chaos elimination in Duffing-Holmes system.

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Section: Back-stepping Methodsmentioning

confidence: 99%

“…Since the pioneering work of Ott, et al [2] proposed the well known OGY control method, the control of chaotic systems has been widely studied. Recently, quite a few techniques and approaches have been successfully applied to chaotic motion control under different conditions and requirements [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Suppression of Chaotic Behavior in Duffing-holmes System using Back-stepping Controller Optimized by Unified Particle Swarm Optimization Algorithm

Gholipour

Khosravi

Mojallali³

2013

IJE

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“…RBFNN is suggested by many scientists as an alternative network to MLPNN. RBFNN is also good at nonlinear mapping (Guo et al, 2006;Ham and Kostanic, 2001). The networks can be trained and they learn the given training set in a shorter period.…”

Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

Evaluation of gravity data by using artificial neural networks case study: Seferihisar geothermal area (Western Turkey)

Kaftan

Şalk

Şenol

2011

Journal of Applied Geophysics

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“…Recently, various methods have been proposed to control chaotic system using neural network (Ramesh and Narayanan, 2001), fuzzy control (Guo et al, 2006;Udawatta et al, 2002), sliding mode control (SMC) (Xu et al, 2004), controlling chaos method (Ott et al, 1990), the state feedback control method and delayed feedback control (Richter and Reinschke, 1998;Sinha et al, 2000), etc. small differences in the system states may lead to exponential divergence of system trajectories with time.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control-based stabilisation and secure communication scheme for hyperchaotic systems

Singh

Sharma

2012

IJAAC

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In this paper, design of a sliding mode control (SMC)-based control scheme is proposed for hyperchaotic systems. Further, the proposed procedure is extended to study robust synchronisation and secure communication scheme for hyperchaotic systems in master-slave configuration. To achieve this goal, a proportional-integral (PI) switching surface is proposed to simplify the task of assigning the performance of the closed-loop error system in sliding motion. Having established the PI switching surface, a sliding mode controller is designed. The proposed controller guarantees the occurrence of sliding motion and achieves asymptotic synchronisation of hyperchaotic systems in masterslave configuration. Then, as an application, the proposed SMC scheme is applied to construct an hyperchaotic secure communication scheme. Finally, numerical simulations are presented to show the efficacy of the proposed approach.Reference to this paper should be made as follows: Singh, S. and Sharma, B.B. (2012) 'Sliding mode control-based stabilisation and secure communication scheme for hyperchaotic systems', Int.

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A radial basis function sliding mode controller for chaotic Lorenz system

Cited by 37 publications

References 10 publications

Suppression of Chaotic Behavior in Duffing-holmes System using Back-stepping Controller Optimized by Unified Particle Swarm Optimization Algorithm

Suppression of Chaotic Behavior in Duffing-holmes System using Back-stepping Controller Optimized by Unified Particle Swarm Optimization Algorithm

Evaluation of gravity data by using artificial neural networks case study: Seferihisar geothermal area (Western Turkey)

Sliding mode control-based stabilisation and secure communication scheme for hyperchaotic systems

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