T-S fuzzy modeling and predictive control and synchronization of chaotic satellite systems

Khan, Ayub; Kumar, Sanjay

doi:10.1080/02286203.2018.1563393

Cited by 41 publications

(28 citation statements)

References 23 publications

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“…4 shows the scheme for the chaotic synchronization systems that use the MSIT2FCA controller. As in [3], the initial conditions for both the master and slave satellite systems are The performance of the synchronization system is calculated using the root mean square error (RMSE), which is written as: where n s is the number of samples and e 1s , e 2s , e 3s is the tracking error for the s th sample. Two examples of chaotic satellite synchronization are demonstrated in the simulation.…”

Section: Illustrative Examplesmentioning

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%

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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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Section: Illustrative Examplesmentioning

confidence: 99%

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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“…x x x   orbits. Example 2: Synchronization of the chaotic satellite system Consider the following chaotic satellite systems in [74]. The chaotic positions of the master system,…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…Synchronization chaotic systems have attracted great interest because of their potential applications in various fields such as electronics, signal processing, secure communication, aerospace, and biological systems [70][71][72][73][74][75][76][77]. Synchronization of chaotic systems is the control of the slave system to mimic the behavior of the master system.…”

Section: Introductionmentioning

confidence: 99%

A Modified Grey Wolf Optimizer for Optimum Parameters of Multilayer Type-2 Asymmetric Fuzzy Controller

Lin

Hong

2020

IEEE Access

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This study presents a modified algorithm of the grey wolf optimizer to solve the problem of learning rate selection in the multilayer type-2 asymmetric fuzzy controller (MT2AFC). The improvements of our modified optimizer are: the best position of the swarm is memorized, thus making the alpha wolves only update when a better position appears in the next iteration; search performance is enhanced by giving more freedom to update the grey wolf position. The proposed optimizer algorithm is then applied to optimize the suitable learning rates for the proposed controller. The multilayer type-2 asymmetric membership function is used in the fuzzy control network to enhance the learning ability and flexibility of the designed network architecture. The gradient descent method is used to adjust the parameters of the proposed MT2AFC controller online. The stability of the system is guaranteed using the Lyapunov approach. Besides, the self-evolving algorithm is used to construct the network structure autonomously. Ultimately, the numerical simulations of the chaotic synchronization systems are carried out to verify the effectiveness of our proposed method. INDEX TERMS grey wolf optimizer, interval type-2 fuzzy neural network, asymmetric membership function, chaotic synchronization systems.

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“…20,21 Satellite acts an important role in the orbits for space technology progresses, military, civil, and scientific activities. A few techniques and methodologies to synchronize and control the satellite systems have been used particularly, predictive control, adaptive control and sliding mode control, [22][23][24][25][26] etc. Synchronization of noninteger order satellite system having momentum-based system are a tedious subject.…”

Section: Introductionmentioning

confidence: 99%

Control and synchronization of fractional‐order chaotic satellite systems using feedback and adaptive control techniques

Kumar

MatoukAhmed

ChaudharyHarindri

et al. 2020

Adaptive Control & Signal

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In this article, we present the basic concepts of fractional calculus and control and synchronization of fractional-order chaotic satellite system . Existence and uniqueness solutions of fractional-order satellite system are discussed and local stability of the system at the equilibrium points are studied. The lowest dimension of chaotic attractor of satellite system is 2.88 which is obtained through utilization of the fractional dynamics and computational simulation. Lyapunov exponents and bifurcation diagrams are drawn to measure the existence of chaos in the system. Feedback control method for chaos control in the fractional-order satellite system is achieved. Synchronization of two identical noninteger order chaotic satellite systems are achieved through adaptive control methodology.

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T-S fuzzy modeling and predictive control and synchronization of chaotic satellite systems

Cited by 41 publications

References 23 publications

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

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

A Modified Grey Wolf Optimizer for Optimum Parameters of Multilayer Type-2 Asymmetric Fuzzy Controller

Control and synchronization of fractional‐order chaotic satellite systems using feedback and adaptive control techniques

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