Fuzzy Sliding-Mode Control Using Adaptive Tuning Technique

Wai, Rong‐Jong

doi:10.1109/tie.2006.888807

Cited by 187 publications

(96 citation statements)

References 17 publications

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“…By the survey of existing literatures for the trajectory tracking design of WMRs, many investigations without taking the internal and external disturbances into account have been studied, and the developed tracking control methods for this trajectory tracking issue focus on four groups: (1) sliding mode control, [7][8][9][10][11][12] (2) feedback linearization, 13,14 (3) backstepping, [15][16][17] and (4) neural networks and fuzzy approaches. [18][19][20][21][22][23] Although these methods worked well without considering the effects of disturbances, there still exist several disadvantages. For example, sliding mode control designs are always with a main drawback of system high-frequency chattering due to switching behavior along a sliding surface, and the feedback linearization-based methods are usually dealing with the trajectory tracking problem of WMRs without taking the internal and external disturbances into consideration.…”

Section: Introductionmentioning

confidence: 99%

Wheeled mobile robot design with robustness properties

Chen

Huang

2018

Advances in Mechanical Engineering

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A trajectory tracking design for wheeled mobile robots is presented in this article. The design objective is to develop one nonlinear robust control law for the trajectory tracking problem of wheeled mobile robots in the presence of modeling uncertainties. The main contribution of this investigation is as follows. Under the effects of modeling uncertainties, an effective control design which can quickly converge tracking errors between the controlled wheeled mobile robot and the desired trajectory is derived mathematically. Generally, it is difficult to develop a nonlinear robust control design for the trajectory tracking problem of wheeled mobile robots due to the complexity and nonlinearity of the wheeled mobile robots' dynamics. Fortunately, based on a series analysis for the tracking error dynamics of the controlled wheeled mobile robot, one promising solution is obtained. For verifying the trajectory tracking performance of this proposed method, two scenarios are utilized in the simulations and the practical tests.

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Section: Introductionmentioning

confidence: 99%

Wheeled mobile robot design with robustness properties

Chen

Huang

2018

Advances in Mechanical Engineering

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“…To deal with this issue, some efforts have been advanced to exploit the ability of robustness, invariance to uncertainties, and resistance to external disturbance for sliding mode control (SMC) [13] to improve the robustness of flying craft control. In recent years, many researchers have investigated the incorporation of SMC into adaptive control and intelligent control methods, such as combining SMC with neural networks [14] or with fuzzy logic systems [15]. Particularly, in the application of SMC, its combination with neural network is validated to overcome an obvious shortcoming; that is, the chattering problem that impedes the application of SMC.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

2017

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A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS) is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA). Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF) neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.

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“…High-order sliding control algorithm is complex. In loworder (first or second order) system control law, there exists a coupling between controller output signal and its derivative, which is not conducive to the design of sliding mode control law [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-based Sliding Mode Control for Permanent Magnet Synchronous Motor

Huang¹,

Zuo²,

Guangdong³

2015

TOEEJ

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Abstract:In this paper, a scheme of designing neural network-based sliding mode controller is proposed for dealing with the chattering phenomenon existing in conventional sliding mode controller because of its discrete control law. Firstly, the sliding mode control law is designed using equivalent control technology. Then the neural network and adaptive control are uesd to delete the chattering of sliding mode controller. The stability of control system is analyzed by Lyapunov stability theory finally. Simulations and experiments demonstrate that the proposed neural network-based sliding mode controller not only achieves better control performance than the conventional sliding mode control system, but also is robust with regard to system parameter variation and external disturbance.

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Fuzzy Sliding-Mode Control Using Adaptive Tuning Technique

Cited by 187 publications

References 17 publications

Wheeled mobile robot design with robustness properties

Wheeled mobile robot design with robustness properties

Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

Neural Network-based Sliding Mode Control for Permanent Magnet Synchronous Motor

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