Generalized projective synchronization of chaotic satellites problem using linear matrix inequality

Farid, Yousef; Moghaddam, Tahmine Vedadi

doi:10.1007/s40435-014-0089-2

Cited by 21 publications

(8 citation statements)

References 37 publications

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“…Theorem 2. The identical chaotic satellite systems (28) and (29) with the unknown parameters are exponentially synchronized via the adaptive control law (33), where the update law for the parameters estimates is given by (31) and k i , (i = 1, 2, … , 6) are positive constants. Also, the parameter estimatesâ,b andĉ exponentially converge to the original values of the parameters a, b, and c respectively, as t → ∞.…”

Section: Adaptive Synchronizationmentioning

confidence: 99%

“…Hamidzadeh and Esmaelzadeh 32 have addressed the control and synchronization of chaotic satellite using active control. Farid and Moghaddam 33 have discussed generalized projective synchronization of chaotic satellites problem using linear matrix inequality. Goeree and Fasse 34 have made deliberation in their papers on sliding mode attitude control of a small satellite for ground tracking maneuvers.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Adaptive Synchronizationmentioning

confidence: 99%

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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show abstract

“…Attitude stabilization of the satellite through an optimal control strategy is presented [3], while the vehicle system dynamics is reviewed for the purpose of developmenting high-speed trains [4]. In making another effort, the generalized projective synchronization of chaotic satellites is suggested through linear matrix inequality [5], where a method of delay compensation in the area of attitude control of flexible spacecraft is presented [6]. The useful practical information regarding the spacecraft dynamics and its control is investigated [7], where the modeling and the corresponding simulation regarding the aerospace vehicle dynamics is suggested [8].…”

Section: Related Workmentioning

confidence: 99%

TADC: a new three-axis detumbling mode control approach

Mazinan

Khalaji

2015

Int. J. Dynam. Control

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A new three-axis detumbling mode control approach, namely TADC in the present research, is investigated to deal with a spacecraft, in a short period of time. In a word, the study considers the problem of detumbling by means of system modelling, while the proposed control approach in agreement with the simulation results can be of novelty with respect to the other related potential benchmarks. The approach proposed here plays an important role in this area to adjust angular velocities in the three axes regarding the system under control to be desirable. In some cases, to guarantee the system stability in the process of missions, the whole of angular velocities are to be accurate. In fact, it aims us to organize a number of programmed maneuvers including the orbital, the thermal and so on to be efficient. The idea behind the approach is organized in line with a linear control approach, since the pulse-width pulse-frequency modulator is employed in association with the control allocation to cope with a set of on-off thrusters. Hereinafter, the number of these on-off thrusters may be increased with respect to the investigated control laws to provide overall accurate performance of the spacecraft through the control allocation. The effectiveness of the approach investigated here is finally considered by organizing four scenarios of the experiments and also comparing the outcomes with a number of potential benchmarks.

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“…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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Generalized projective synchronization of chaotic satellites problem using linear matrix inequality

Cited by 21 publications

References 37 publications

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

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

TADC: a new three-axis detumbling mode control approach

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

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