Chaotic satellite synchronization using neural and nonlinear controllers

Sarcheshmeh, Saeed Farzami; Esmaelzadeh, Reza; Afshari, Mehdi

doi:10.1016/j.chaos.2017.02.002

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…Based on these backgrounds, in this work, a non‐linear auto‐regressive moving average L2 (NARMA‐L2) controller is proposed for the BFC‐NSFCL to augment the FRT capability of DFIG‐based WFs. The NARMA‐L2 has been used for different dynamic systems control action [46, 47], but to the best of our knowledge, it has never been used to control the operation of the FCL and that is one of the novelties of this work. To evaluate the efficacy of the NARMA‐L2 controlled BFC‐NSFCL (NL2‐BFC‐NSFCL), its performances are compared with conventional FCLs such as SDBR [29, 48], BFCL [30], and proportional–integral (PI) control‐based BFC‐NSFCL (PI‐BFC‐NSFCL).…”

Section: Introductionmentioning

confidence: 99%

Performance improvement of DFIG‐based wind farms using NARMA‐L2 controlled bridge‐type flux coupling non‐superconducting fault current limiter

Islam¹,

Hasan²,

Hasan³

et al. 2020

IET gener. transm. distrib.

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Doubly-fed induction generators (DFIGs) have drawn prominent interest in the field of wind power generation, but they are vulnerable to grid faults. Grid codes mandate DFIGs to employ a sort of fault ride-through (FRT) technique during faults. Fault current limiters (FCLs) always help to augment the FRT capability of DFIGs and a non-linear controller boosts their performances. In this study, a non-linear auto-regressive moving average-L2 (NARMA-L2) controller-based bridge-type flux coupling non-superconducting FCL (BFC-NSFCL) is proposed to enhance the FRT capability of the wind farm. The authors analysed the performance of the proposed NARMA-L2-based BFC-NSFCL (NL2-BFC-NSFCL) against that of the conventionally used series dynamic braking resistor (SDBR), bridge-type FCL (BFCL), and proportional-integral (PI) controller-based BFC-NSFCL (PI-BFC-NSFCL). They tested the performance of the NL2-BFC-NSFCL through multiple temporary and permanent fault scenarios and carried out the mathematical and graphical analysis in MATLAB/Simulink platform. They found that the proposed NL2-BFC-NSFCL's performance surpasses the performances of the SDBR, the BFCL, and the PI-BFC-NSFCL. Moreover, the NL2-BFC-NSFCL has faster system recovery capability after the occurrence of any fault than other competitors.

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Section: Introductionmentioning

confidence: 99%

Performance improvement of DFIG‐based wind farms using NARMA‐L2 controlled bridge‐type flux coupling non‐superconducting fault current limiter

Islam¹,

Hasan²,

Hasan³

et al. 2020

IET gener. transm. distrib.

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“…A satellite system exhibits chaotic behavior under the influence of gravitational and geomagnetic fields and solar radiation pressure [1]. Synchronization with other satellites increases the accuracy of satellite missions.…”

Section: Introductionmentioning

confidence: 99%

“…A chaotic satellite is a nonlinear system with a specific sensitivity to the initial conditions, a fractal structure and which is non-periodic [2]. Recent studies have proposed a variety of techniques and methodologies to synchronize a chaotic satellite system, such as fuzzy control [3]- [5], predictive control [6], sliding mode control [7], control using a neural network [1], and a linear matrix inequality [8]. However, most…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Interval Type-2 Fuzzy Asymmetric CMAC Design to Synchronize Chaotic Satellite Systems Using a Modified Grey Wolf Optimizer

Lin

Hong

2020

IEEE Access

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This study presents a self-organizing interval type-2 fuzzy asymmetric cerebellar model articulation controller (MSIT2FAC) design for synchronizing chaotic satellite systems that use a modified grey wolf optimizer. The proposed control system uses MSIT2FAC as the main controller (which mimics an ideal controller) and a robust compensation controller (which addresses the approximation error between the ideal controller and the main controller). The self-organizing algorithm is used to generate the first network layer. In subsequent iterations, it autonomously increases or decreases the number of network layers using the tracking error. The adaptive laws for adjusting the parameters for the fuzzy rule for the proposed system are derived using the gradient descent method. The optimal learning rates for the adaptive laws are achieved using a modified grey wolf optimizer. The Lyapunov stability analysis guarantees the stability of the proposed algorithm. Finally, the numerical simulation results illustrate the effectiveness of the proposed method. INDEX TERMS Interval type-2 fuzzy neural network, self-organizing algorithm, cerebellar model articulation controller, asymmetric membership function, chaotic satellite.

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“…Yadmellat and Nikravesh have proposed a neural network–based output-feedback control method for nonlinear chaotic systems [ 49 ]. In another paper, Sarcheshmeh et al designed two neural controllers to synchronize two master and slave chaotic satellites [ 50 ]. In order to suppress the disturbances in the chaotic systems, it is necessary to design an adaptive controller.…”

Section: Introductionmentioning

confidence: 99%

Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Four-Dimensional Chaotic System with Hidden Attractors

Jahanshahi

Shahriari-kahkeshi

Alcaraz

et al. 2019

Entropy

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Today, four-dimensional chaotic systems are attracting considerable attention because of their special characteristics. This paper presents a non-equilibrium four-dimensional chaotic system with hidden attractors and investigates its dynamical behavior using a bifurcation diagram, as well as three well-known entropy measures, such as approximate entropy, sample entropy, and Fuzzy entropy. In order to stabilize the proposed chaotic system, an adaptive radial-basis function neural network (RBF-NN)–based control method is proposed to represent the model of the uncertain nonlinear dynamics of the system. The Lyapunov direct method-based stability analysis of the proposed approach guarantees that all of the closed-loop signals are semi-globally uniformly ultimately bounded. Also, adaptive learning laws are proposed to tune the weight coefficients of the RBF-NN. The proposed adaptive control approach requires neither the prior information about the uncertain dynamics nor the parameters value of the considered system. Results of simulation validate the performance of the proposed control method.

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Chaotic satellite synchronization using neural and nonlinear controllers

Cited by 13 publications

References 14 publications

Performance improvement of DFIG‐based wind farms using NARMA‐L2 controlled bridge‐type flux coupling non‐superconducting fault current limiter

Performance improvement of DFIG‐based wind farms using NARMA‐L2 controlled bridge‐type flux coupling non‐superconducting fault current limiter

Self-Organizing Interval Type-2 Fuzzy Asymmetric CMAC Design to Synchronize Chaotic Satellite Systems Using a Modified Grey Wolf Optimizer

Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Four-Dimensional Chaotic System with Hidden Attractors

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