Decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems

Hung, Lon-Chen; Chung, Habong

doi:10.1016/j.ijar.2006.08.002

Cited by 57 publications

(35 citation statements)

References 23 publications

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“…We will apply the conventional adaptive sliding mode controller [21], a type-I fuzzy neural network based sliding mode controller [22][23] and an interval type-II fuzzy neural network identification based gain adaptive sliding mode controller, to let the Micro Aircraft Vehicle system track the reference attitude trajectory.…”

Section: Resultsmentioning

confidence: 99%

Modeling and designing intelligent adaptive sliding mode controller for an Eight-Rotor MAV

Chen¹,

Li²

2013

International Journal of Aeronautical and Space Sciences

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This paper focuses on the modeling and intelligent control of the new Eight-Rotor MAV, which is used to solve the problem of the low coefficient proportion between lift and gravity for the Quadrotor MAV. The Eight-Rotor MAV is a nonlinear plant, so that it is difficult to obtain stable control, due to uncertainties. The purpose of this paper is to propose a robust, stable attitude control strategy for the Eight-Rotor MAV, to accommodate system uncertainties, variations, and external disturbances. First, an interval type-II fuzzy neural network is employed to approximate the nonlinearity function and uncertainty functions in the dynamic model of the Eight-Rotor MAV. Then, the parameters of the interval type-II fuzzy neural network and gain of sliding mode control can be tuned on-line by adaptive laws based on the Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. The validity of the proposed control method has been verified in the Eight-Rotor MAV through real-time experiments. The experimental results show that the performance of the interval type-II fuzzy neural network based adaptive sliding mode controller could guarantee the EightRotor MAV control system good performances under uncertainties, variations, and external disturbances. This controller is significantly improved, compared with the conventional adaptive sliding mode controller, and the type-I fuzzy neural network based sliding mode controller.

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Section: Resultsmentioning

confidence: 99%

Modeling and designing intelligent adaptive sliding mode controller for an Eight-Rotor MAV

Chen¹,

Li²

2013

International Journal of Aeronautical and Space Sciences

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“…The main idea behind the decoupled strategy is to decouple a nonlinear system appearing in the form of (6) into two subsystems as Electrical and Mechanical in the form of (10,11). The Electrical subsystem is chosen as a primary target while the Mechanical subsystem is used as a secondary target.…”

Section: Decoupled Fuzzy Sliding Mode Control Designmentioning

confidence: 99%

“…Reduced chattering may be achieved without sacrificing robust performance by combining the attractive features of fuzzy logic with SMC [9][10]. Fuzzy Logic Control is a non-conventional and robust control law.…”

Section: Introductionmentioning

confidence: 99%

Decoupled Fuzzy Sliding Mode Control for a Synchronous Motor Speed Control

Abdelsalam¹,

Areed²

2012

IJCA

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This paper introduces a new technique for controlling the speed of Permanent Magnet Synchronous Motor (PMSM). This technique depends on two well known control methods; the first control method is known the variable structure system (VSS), which has a main advantage known as an sliding mode property. The second method depends on fuzzy logic control. A combination between the two mentioned above methods is suggested in this paper. In addition for simplifying the suggested technique, the model of PMSM is decoupled. Simulation results illustrate the validity and the effectiveness of the new suggested technique.

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“…However, there are several parameters required in the decoupling sliding surface. Since these parameters are not easily identified, several estimators for those parameters have been developed in [10,11,16]. Instead of estimating those parameters, in this study, we intent to directly employ adaptive fuzzy control schemes for the design of the decoupling sliding surface [13].…”

Section: Introductionmentioning

confidence: 99%

Direct Adaptive Fuzzy Sliding Mode Control for Under-actuated Uncertain Systems

Hsueh

Tseng

et al. 2015

Int. J. Fuzzy Log. Intell. Syst.

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The development of the control algorithms for under-actuated systems is important. Decoupled sliding mode control has been successfully employed to control under-actuated systems in a decoupling manner with the use of sliding mode control. However, in such a control scheme, the system functions must be known. If there are uncertainties in those functions, the control performance may not be satisfactory.In this paper, the direct adaptive fuzzy sliding mode control is employed to control a class of under-actuated uncertain systems which can be regarded as a combination of several subsystems with one same control input. By using the hierarchical sliding control approach, a sliding control law is derived so as to make every subsystem stabilized at the same time. But, since the system considered is assumed to be uncertain, the sliding control law cannot be readily facilitated. Therefore, in the study, based on Lyapunov stable theory a fuzzy compensator is proposed to approximate the uncertain part of the sliding control law. From those simulations, it can be concluded that the proposed compensator can indeed cope with system uncertainties. Besides, it can be found that the proposed compensator also provide good robustness properties.

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Decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems

Cited by 57 publications

References 23 publications

Modeling and designing intelligent adaptive sliding mode controller for an Eight-Rotor MAV

Modeling and designing intelligent adaptive sliding mode controller for an Eight-Rotor MAV

Decoupled Fuzzy Sliding Mode Control for a Synchronous Motor Speed Control

Direct Adaptive Fuzzy Sliding Mode Control for Under-actuated Uncertain Systems

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