• Journal of Advanced Marine Engineering and Technology
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• v.20 no.2
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• pp.33-39
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• 1996

This paper presents the linearized fuzzy modeling technique of nonlinear dynamical system and the stability analysis of fuzzy control system. Firstly, the nonlinear system is partitionized by multiple linear fuzzy subcontrol systems based on fuzzy linguistic variables and fuzzy rules. Secondly, the disturbance adaptaion controllers which guarantee the global asymptotic stability of each fuzzy subsystem by an optimal feedback control law are designed and the stability analysis procedures of the total fuzzy control system using Lyapunov functions and eigenvalues are discussed in detail through a given illustrative example.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.6
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• pp.946-961
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• 2004

A new adaptive controller which combines a model reference adaptive controller (MRAC) and a fuzzy controller is developed for unstable nonlinear time-invariant systems. The fuzzy controller is used to analyze and to compensate the nonlinear time-invariant characteristics of the plant. The MRAC is applied to control the linear time-invariant subsystem of the unknown plant, where the nonlinear time-invariant plant is supposed to comprise a nonlinear time-invariant subsystem and a linear time-invariant subsystem. The stability analysis for the overall system is discussed in view of global asymptotic stability. In conclusion. the unknown nonlinear time-invariant plant can be controlled by the new adaptive control theory such that the output error of the given plant converges to zero asymptotically.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.1-6
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• 2012

In this paper, an adaptive fuzzy control system is proposed for the speed control of the ASV (Autonomous Surface Vehicle) with nonaffine nonlinear system dynamics. We consider the turning speed of the screw propeller to be the control input instead of thrust so that we do not have to know the exact function between turning speed and thrust. But in this case, the ASV becomes a nonaffine nonlinear system because thrust is a nonlinear function of the turning speed. To solve this problem, we propose a Takagi-Sugeno fuzzy-model-based control system and simulation studies are performed. Simulation results show the effectiveness of the proposed control scheme.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.68-74
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• 2008

In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. Then a neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for nonlinear effects. Two neural-fuzzy control schemes based on two well-known neural network control schemes, the feedback error learning scheme and the reference compensation technique scheme as well as the standard PD-like fuzzy control are studied. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.583-595
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• 2008

In this paper, we introduce a fuzzy nonlinear feedback approach to the control of a class of chaotic dynamical systems. The fuzzy Parallel Distributed Compensation with Reduced Rule Base approach (PDC_RRB) is proposed. The design procedure is conceptually simple and considered to a nonlinear optimal and robust control problem due to the nonlinear nature of the Takagi-Sugeno (TS) fuzzy system. Simulation results are provided to show the effictiveness of the proposed methodology.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.61-67
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• 2008

In this paper proposed the solar tracking system to use a fuzzy based on PC in order to increase an output of the PV array. The solar tracking system operated two DC motors driving by signal of photo sensor. The control of dual axes is not an easy task due to nonlinear dynamics and unavailability of the parameters. Recently, artificial intelligent control of the fuzzy control, neural-network and genetic algorithm etc. have been studied. The fuzzy control made a nonlinear dynamics to well perform and had a robust and highly efficient characteristic about a parameter variable as well as a nonlinear characteristic. Hence the fuzzy control was used to perform the tracking system after comparing with error values of setting-up. nonlinear altitude and azimuth. In this paper designed a fuzzy controller for improving output of PV array and evaluated comparison with efficient of conventional PI controller. The data which were obtained by experiment were able to show a validity of the proposed controller.

• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.193-197
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• 2002

This paper suggests a method to design TSK(Takagi-Sugeno-Kang) fuzzy nonlinear control system for automatic steering system which contains the nonlinear component of ship's maneuvering equation. A TSk fuzzy model can be identified using input-output data and represent a nonlinear system very well. A TSK fuzzy controller can be designed systematically from a TSK fuzzy model because the consequent part of TSK fuzzy rule is a linear input-output equation having a constant term. Therefore, this paper suggests the method identifying the TSK fuzzy model and designing the TSK fuzzy controller based on the TSK fuzzy model for ship steering.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.14-21
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• 2001

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. In indirect adaptive fuzzy control, based on the proved approximation capability of fuzzy systems, they are used to capture the unknown nonlinearities of the plant. Until now, most of the papers in the field of controller design for nonlinear system considers the affine system using fuzzy systems which have fixed grid-rule structure. We proposes a dynamic fuzzy rule insertion scheme where fuzzy rule-base grows as time goes on. With this method, the dynamic order of the controller reduces dramatically and an appropriate number of fuzzy rules are found on-line. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed-loop system is guaranteed.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.437-440
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• 1998

In this paper, the design method of fuzzy neural network(FNN) controller using indirect adaptive control technique is presented for controlling chaotic nonlinear systems. Firstly, the fuzzy model identified with a FNN in off-line process. Secondly, the trained fuzzy model tunes adaptively the control rules of the FNN controller in on-line process. In order to evaluate the proposed control method, Indirect adaptive control method is applied to the representative continuous-time chaotic nonlinear systems, that is, the Duffing system and the Lorenz system. Simulations are done to verify the effectivencess of controller.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2898-2901
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• 1999

This paper describes the design of nonlinear fuzzy I+PD controller using simplified indirect inference method. First, the fuzzy I+PD controller is derived from the conventional continuous time linear I+PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional I+PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. Particularly when the process to be controlled is nonlinear When the SIIM is applied, the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one Proposed by D. Misir et at.