The robust adaptive control of chaotic systems with unknown parameters and external disturbance via a scalar input

Luo, Runzi

doi:10.1002/acs.2540

Cited by 10 publications

(10 citation statements)

References 16 publications

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“…In Eqs. 17, (18), second terms are calculated as: (22) where z η is also changed adaptively as: (23) where 1 > φ and 1 < ς are appropriate constants. With above updating rules, the output of the network converges to the target stable fixed point.…”

Section: Proposed Control Schemementioning

confidence: 99%

“…Backstepping is one of the most promising Lyapunov based adaptive robust control techniques, which has been used for stabilization and tracking control of continuous time [31,23,24] and discrete time [21] chaotic systems as well as of nonlinear chaotic systems with bounded uncertainties and external disturbances [2,22]. Adaptive backstepping controllers have a good performance for stabilizing the uncertain dynamical systems but ignore the parameter estimation errors in updating rules.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Neural Network Control Scheme for Stabilizing Chaos to the Stable Fixed Point

Gokce¹,

Uyaroğlu

2017

ITC

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A neural network control scheme with a novel adaptive learning rate is proposed to stabilize the chaotic trajectory of the chaotic system to a stable fixed point. A new approach is proposed to determine the stability of the fixed points in which the eigenvalues of the Jacobian matrix of the chaotic system at different values of the chaoticity parameter are evaluated and a look-up table is created to find a suitable fixed point that has a negative Lyapunov exponent. During learning phase of the neural network, weight parameters are adjusted so that the chaotic trajectory converges to a stable fixed point and the maximum Lyapunov exponent of the controlled system becomes negative. The effectiveness of the proposed method is investigated through simulation studies on 2 dimensional Ikeda map, which is produced by a semiconductor laser system.

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Section: Proposed Control Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Neural Network Control Scheme for Stabilizing Chaos to the Stable Fixed Point

Gokce¹,

Uyaroğlu

2017

ITC

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“…Therefore, control of such unpredictable systems caught attention of researcher since 1990 s . Runzi Luo [16] investigated control of unpredictable behavior zhang hyperchaotic system with model uncertainties, external noise and anonymous parameters. In 2019, Omid Mofid et.…”

Section: Introductionmentioning

confidence: 99%

Observer and descriptor satisfying incremental quadratic constraint for class of chaotic systems and its applications in a quadrotor chaotic system

Sabir

Marwan

Ahmad

et al. 2020

Chaos, Solitons & Fractals

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A drone based on four rotors is considered in this research paper. Its chaotic solution is shown bounded in an inscribed sphere whose vertices are tangent to faces of octahedron. Based on concept of constrained optimization; Linear Matrix Inequalities (LMIs) satisfying quadratic constraint increment multiplier matrix σ m , state observers and descriptors with estimated parameter is calculated. Moreover, an image file is decrypted by designing description for mentioned chaotic system and then encrypted on its receiver end. Furthermore, an electric circuit is designed for chaotic quadrotor using LTspice and is fitted into wireless flying robot to observe its dynamics in bounded rectangular region.

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“…A system is called as the chaotic system, provided that it exhibits chaos phenomenon. Due to its powerful potential applications in secure communications, biological systems, information processing, etc., the control of chaotic system has attracted extensive attention of scholars in recent 20 years, and many e cient approaches have been presented for controlling chaos, such as OGY control [1], impulsive control [2], fuzzy control [3,4], sliding mode control [5,6], adaptive control [7,8], composite learning control [9,10], and so on. Nowadays, many papers about controlling chaotic systems have been published.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fast Convergence Control Scheme for a Class of 3D Chaotic Systems with Uncertain Parameters and External Disturbances

Luo

et al. 2020

Complexity

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This paper studies the control of a class of 3D chaotic systems with uncertain parameters and external disturbances. A new method which is referred as the analytical solution approach is firstly proposed for constructing Lyapunov function. Then, for suppressing the trajectories of the 3D chaotic system to its equilibrium point 00,0,0, a novel fast convergence controller containing parameter λ which determines the convergence rate of the system is presented. By using the designed Lyapunov function, the stability of the closed-loop system is proved via the Lyapunov stability theorem. Computer simulations are employed to a new chaotic system to illustrate the effectiveness of the theoretical results.

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The robust adaptive control of chaotic systems with unknown parameters and external disturbance via a scalar input

Cited by 10 publications

References 16 publications

An Adaptive Neural Network Control Scheme for Stabilizing Chaos to the Stable Fixed Point

An Adaptive Neural Network Control Scheme for Stabilizing Chaos to the Stable Fixed Point

Observer and descriptor satisfying incremental quadratic constraint for class of chaotic systems and its applications in a quadrotor chaotic system

A Novel Fast Convergence Control Scheme for a Class of 3D Chaotic Systems with Uncertain Parameters and External Disturbances

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