Adaptive gain fuzzy sliding mode control for the synchronization of nonlinear chaotic gyros

Roopaei, Mehdi; Jahromi, Mansoor Zolghadri; Jafari, Shahram

doi:10.1063/1.3072786

Cited by 43 publications

(21 citation statements)

References 38 publications

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“…To illustrate the effectiveness of the proposed control method (NAF-PIDSMC) (17), in simulation we compared with two other controllers:…”

Section: Fig 2 Block Diagram Of the Proposed Controllermentioning

confidence: 99%

“…However, these methods used saturation to replace sign function in the reaching phase of SMC, therefore, the tracking error will be increased and the feedback of the system will be reduced. (2) Using fuzzy logic system to adaptively tune the sliding mode gain in the discontinuous part of conventional SMC [17,18]. As a result, the chattering phenomenon is attenuated.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Fuzzy PID Sliding Mode Controller of Uncertain Robotic Manipulator

Tran

Kang

2015

Intelligent Computing Theories and Methodologies

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Abstract. This paper presents a novel adaptive fuzzy sliding mode controller (AFSMC) that combines a PID sliding mode control (SMC) and adaptive fuzzy control (AFC) for robotic manipulator in the presence of uncertainties and external disturbances. First, the controller is developed based on PID sliding mode framework. Then, two fuzzy logic systems are applied, a fuzzy logic system is used to replace the discontinuous control term, and an adaptive fuzzy inference engine is utilized to approximate the unknown term in the equivalent control part of SMC. Thus, the system not only can handle the large uncertainties and does not require the knowledge of their upper bounds to be known in advance, but can also attenuate the chattering phenomenon in conventional SMC without degrading the system robustness. Finally, the simulation results of a two-link robot manipulator are presented to demonstrate the effectiveness of the proposed control method.

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“…To illustrate the effectiveness of the proposed control method (NAF-PIDSMC) (17), in simulation we compared with two other controllers:…”

Section: Fig 2 Block Diagram Of the Proposed Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy PID Sliding Mode Controller of Uncertain Robotic Manipulator

Tran

Kang

2015

Intelligent Computing Theories and Methodologies

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“…In [19], a robust fuzzy sliding mode controller for the speed tracking of a permanent magnet synchronous motor is proposed to improve the robustness under motor parameter and load torque variations. Furthermore, a Type 2 fuzzy sliding mode controller is designed for synchronization of two nonlinear chaotic gyros in the presence of model uncertainties and external disturbances [20]. Moreover, a fractional order fuzzy sliding mode controller is developed for robotic manipulators [21].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Sliding Mode Observer with Grey Prediction for the Estimation of the State-of-Charge of a Lithium-Ion Battery

et al. 2015

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Abstract:We propose a state-of-charge (SOC) estimation method for Li-ion batteries that combines a fuzzy sliding mode observer (FSMO) with grey prediction. Unlike the existing methods based on a conventional first-order sliding mode observer (SMO) and an adaptive gain SMO, the proposed method eliminates chattering in SOC estimation. In this method, which uses a fuzzy inference system, the gains of the SMO are adjusted according to the predicted future error and present estimation error of the terminal voltage. To forecast the future error value, a one-step-ahead terminal voltage prediction is obtained using a grey predictor. The proposed estimation method is validated through two types of discharge tests (a pulse discharge test and a random discharge test). The SOC estimation results are compared to the results of the conventional first-order SMO-based and the adaptive gain SMO-based methods. The experimental results show that the proposed method not only reduces chattering, but also improves estimation accuracy.

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“…For this reason, theoretical studies of chaos control and synchronization with simple adaptive control emerged [39][40][41][42][43][44][45][46][47][48][49]. In [45] a novel adaptive controller for achieving chaos and hyperchaos synchronization was proposed.…”

Section: Introductionmentioning

confidence: 99%