Rule regulation of fuzzy sliding mode controller design: direct adaptive approach

Chen, Jen‐Yang

doi:10.1016/s0165-0114(99)00111-6

Cited by 36 publications

(20 citation statements)

References 14 publications

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“…The main objective is to design a discrete time sliding mode control strategy and enhanced by a stable adaptive fuzzy inference system to cope with modeling inaccuracies and external disturbances that can arise. Many researches on introducing the concept of fuzzy logic and especially fuzzy approximators into sliding mode control have been developed in the past years [15][16][17][18]. Historically, fuzzy logic systems have been proposed in an attempt to control nonlinear systems whose parameters are inaccuracy, and presenting neglected dynamics as well as time varying systems [19].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Discrete-time Fuzzy Sliding Mode Control For a Class of Chaotic Systems

Medhaffar¹,

Feki²,

Derbel³

2017

Adv. sci. technol. eng. syst. j.

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In this paper, we propose an adaptive discrete-time fuzzy sliding mode control for a class of chaotic systems. For this aim, a discrete sliding mode controller and a fuzzy system are combined to achieve an adequate control. The Laypunov stability theorem is used to testify the asymptotic stability of the whole system and the consequence parameters of the adaptive fuzzy system are tuned on-line by adaptive laws. The simulation example of the 3D Henon chaotic model is giving to confirm the effectiveness and the robustness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Adaptive Discrete-time Fuzzy Sliding Mode Control For a Class of Chaotic Systems

Medhaffar¹,

Feki²,

Derbel³

2017

Adv. sci. technol. eng. syst. j.

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“…The traditional PID linear control method based on accurate mathematical model have the defects at the aspects of systems' adaptivity and robust control [2,3]. In order to realize the adaptive control of the system with above characteristics, domestic and foreign scholars have proposed a variety of nonlinear control methods, including fuzzy control, expert system control and neural network control method [4][5][6]. Therefore, the computer control software design methods have become a hot issue requiring to be researched in control field, and concentrated widespread concern of many experts and scholars.…”

Section: Introductionmentioning

confidence: 99%

Computer control software design model based on particle swarm optimization and PID algorithm

Qian¹,

Hu²,

Fu³

et al. 2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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Abstract. The optimal design for computer control software is studied. Since computer control software is susceptible to interference during the control process, a computer control software design model based on improved PID algorithm is proposed. The PID algorithm is combined with particle swarm algorithm to calculate PID control parameters, which is viewed as evolutionary particles of the particle population, and given a certain flight speed in the search space, the speed of the particles will be adjusted iteratively and dynamically in accordance with the experience of population's evolution calculation, in order to achieve computer control software design. The simulation results show that the proposed algorithm applied for computer control software design, can improve the control precision and meet the actual needs of computer control.

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“…In literature survey, it is found that the combination of the methods are proposed so that sliding mode algorithm is used in the design of Soft computing based controllers and the inputs to the controller are error and change in error and the inherent stability property of sliding mode controller is not utilized [4]- [12], [16], [17]. Presented in this paper is the application of fuzzy logic in the design of sliding mode controller to regulate the output voltage of a buck converter.…”

Section: Introductionmentioning

confidence: 99%

A Novel Soft Computing Based Algorithm for the Control of Dynamic Uncertain Systems - An Application to DC-DC Converters

Kareem¹,

Azeem²

2011

IJAIA

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Rule regulation of fuzzy sliding mode controller design: direct adaptive approach

Cited by 36 publications

References 14 publications

Adaptive Discrete-time Fuzzy Sliding Mode Control For a Class of Chaotic Systems

Adaptive Discrete-time Fuzzy Sliding Mode Control For a Class of Chaotic Systems

Computer control software design model based on particle swarm optimization and PID algorithm

A Novel Soft Computing Based Algorithm for the Control of Dynamic Uncertain Systems - An Application to DC-DC Converters

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