• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.762-767
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• 2008

This paper introduces a $L_{\infty}$-gain state feedback fuzzy controller design for the time delay nonlinear system represented by Takagi-Sugeno(T-S) fuzzy model. First, the T-S fuzzy model is employed to represent the time delay nonlinear system. Next based on the fuzzy model, a fuzzy state feedback controller is developed to achieve $L_{\infty}$-gain performance. Finally, sufficient conditions are derived for $L_{\infty}$-gain performance. The sufficient conditions are formulated in the format of linear matrix inequalities (LMIs). The effectiveness of the proposed controller design methonology is finally demonstrated through numerical simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.320-325
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• 1998

Underwater robotic vehicles (URVs) have been an important tool for various underwater tasks because they have greater speed, endurance, depth capability, and safety than human divers. As the use of such vehicles increases, the vehicle control system becomes one of the most critical subsytems to increase autonomy of the vehicle. The vehicle dynamics are nonlinear and their hydrodynamic coefficients are often difficult to estimate accurately. In this paper a new type of fuzzy model-based controller based on Takagi-Sugeno-Kang fuzzy model is designed and applied to the control of of an underwater robotic vehicle. The proposed fuzzy controller : 1) is a nonlinear controller, but a linear state feedback controller in the consequent of each local fuzzy control rule ; 2) can guarantee the stability of the closed-loop fuzzy system ; 3) is relatively easy to implement. Its good performance as well as its robustness to the change of parameters have been shown and compared with the re ults of conventional linear controller by simulation.

• Journal of Power System Engineering
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• v.2 no.2
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• pp.47-54
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• 1998

Underwater robotic vehicles(URVs) have been an important tool for various underwater tasks such as pipe-lining, data collection, hydrography mapping, construction, maintenance and repairing of undersea equipment, etc because they have greater speed, endurance, depth capability, and safety than human divers. As the use of such vehicles increases, the vehicle control system is one of the most critical subsystems to increase autonomy of the vehicle. The vehicle dynamics are nonlinear and their hydrodynamic coefficients are often difficult to estimate accurately. It is desirable to have an intelligent vehicle control system because the fixed-parameter linear controller such as PID may not be able to handle these changes promptly and result in poor performance. In this paper we described and analyzed a new type of fuzzy model-based controller which is designed for underwater robotic vehicles and based on Takagi-Sugeno-Kang(TSK) fuzzy model. The proposed fuzzy controller: 1) is a nonlinear controller, but a linear state feedback controller in the consequent of each local fuzzy control rule; 2) can guarantee the stability of the closed-loop fuzzy system; 3) is relatively easy to implement. Its good performance as well as its robustness to parameter changes will be shown and compared with those of the PID controller by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1500-1505
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• 2004

This paper presents the design and control of a small Brushless DC (BLDC) Motor for entertainment robots. In order to control the developed BLDC motor, Adaptive Fuzzy Control (AFC) scheme via Parallel distributed Compensation(PDC) is developed for the multi- input/multi-output plant model represented by the Takagi-Sugeno(TS) model. The alternative AFC scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. The suggested design technique is applied to the velocity control of a developed small BLDC motor for entertainment robots.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.152-159
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• 2005

For high-speed data communication in band-limited channels, main of the bit error are fading and ISI(Inter-Symbol Interference). The common way of dealing with ISI is using equalization in the receiver. In this thesis, channel adaptive equalizer which uses Fuzzy Stochastic Gradient(FSG) and Constant Modulus Algorithm(CMA) is nonlinear equalizer, or Blind equalizer, that works directly on the signals with no training sequences required. This equalizer employs Takagi-Sugeno's fuzzy model that uses the FSG algorithm, to automatically regulate the step size of the descent gradient vector, combining fast convergence rate and low mean square error(MSE), and the CMA which is a special case of Godard's algorithm, to having multiple dispersion constants($R_p$).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.415-418
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• 2003

Fuzzy modelling has the approximation property far the given input-output relationship. Especially, Takagi-Sugeno fuzzy models are widely used because they show very good performance in the nonlinear function approximation problem. But generally there is not the systematic method incorporating the human expert's knowledge or experience in fuzzy rules and it is not easy to End the membership function of fuzzy rule to minimize the output error as well. The ANFIS (Adaptive Network-based Fuzzy Inference Systems) is one of the neural network based fuzzy modelling methods that can be used with various type of fuzzy rules. But in this model, it is the problem to End the optimum number of fuzzy rules in fuzzy model. In this paper, a new fuzzy modelling method based on the ANFIS and pruning techniques with the measure named impact factor is proposed and the performance of proposed method is evaluated with several simulation results.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.1
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• pp.34-39
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• 2013

This paper proposes the implementation of robust fuzzy controller for designing intelligent wind farm and mitiagating the fluctuation of wind power generator. The existing researches are limited to individual wind turbine with variable speed so that it is necessary to study the multi-agent wind turbine power system. The scopes of these studies include from the arrangements of each power turbine to the control algorithms for the wind farm. For solving these problems, we introduce the composition of intelligent wind farm and use the T-S (Takagi-Sugeno) fuzzy model which is suitable for designing fuzzy controller. The control object in wind farm enables the minimizing the fluctuation of wind power generator. Simulation results for wind fram which is modelled as mathematically are demonstrated to visualize the feasibility of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1037-1041
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• 2004

This paper examines the problem of designing an $H_{\infty}$ fuzzy state-feedback controller for a class of uncertain nonlinear descriptor systems which is described by a Takagi-Sugeno (TS) fuzzy model. Based on a linear matrix inequality (LMI) approach, we develop an $H_{\infty}$ state-feedback controller which guarantees the $L_2$-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value for this class of systems. A numerical example is provided to illustrate the design developed in this paper.

• 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.2 no.3
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• pp.231-236
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• 2002

In this paper, we present an analysis and design method fur the control of input-saturated nonlinear systems with the time-delay. The target system is represented by Takagi-Sugeno (T-S) fuzzy model and the parallel distributed compensation (PDC) controller is designed to guarantee the local stability of the equilibrium point. We derive the sufficient condition for the local stability by applying Lyapunov-krasovskii theorem and this condition is converted into the LMI problem.