• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.330-336
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• 2005

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inputs. It is shown that all the error signals in the learning system are bounded and the repetitive robot motion converges to the desired one exponentially fast with guaranteed convergence rate. An engineering workstation based control system is built to verify the effectiveness of the proposed control scheme.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.269-282
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• 2007

This paper presents a robust control method via the intelligent adaptive backstepping design technique for stable walking of nine-link biped robots with unknown model uncertainties and external disturbances. In our control structure, the self recurrent wavelet neural network(SRWNN) which has the information storage ability is used to observe the uncertainties of the biped robots. The adaptation laws for all weights of the SRWNN are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Also, we prove that all signals in the closed-loop adaptive system are uniformly ultimately bounded. Through computer simulations of a nine-link biped robot with model uncertainties and external disturbances, we illustrate the effectiveness of the proposed control system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.125-130
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• 1995

Recently, the vehicle engine requried precision control of Air-Fuel rate and rigid restriction of exhaust gas. Therefore, we demanded excellent measuring equipment so as to improve of engine performance. Specially, throttle valve control is very important part in the engine control, because structure of engine dynamometer system is very important part in the engine control, because structure of engine dynamometer system is very complicate and it has nonlinear elements which is influenced of disturbance about vibration, a heat, a cooling, energy loss so on. In this study, we propose the method that the control technique using Fuzzy Look-up table and we obtained the satisfying result from realized the control system.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1196-1201
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• 2014

This paper presents an exploration on the effect of wind turbine contribution to the frequency control of individual systems that can be used for efficient power production in India. The research includes the study of Permanent Magnet Synchronous Generator (PMSG), in wind farms. The WTs are tested for inertia and for droop responses with intelligent fuzzy logic controllers (FLC) that choose Double Input Single Output (DISO) strategy that automatically sets gain constants, as well as combined responses for the WTs. Quantitative analyses are presented for the WTs for benefits and drawbacks including appropriate selection parameters. The analysis includes inertia, droop and combined inertia, droop schemes. The reconnaissance also incorporates inertia with FLC, droop with FLC, inertia and droop with FLC schemes for detailed study of WTs, so as to forecast and achieve proper frequency control. Moreover, the analysis provides the best suited method for frequency control in PMSG.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.2
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• pp.83-88
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• 2006

This paper proposes a method that minimizes the consumed energy by searching the optimal locations of the mass centers of the biped robot's links using Genetic Algorithm. This paper presents a learning controller for repetitive gait control of the biped robot. The learning control scheme consists of a feedforward learning nile and linear feedback control input for stabilization of learning system. The feasibility of learning control to the biped robotic motion is shown via computer simulation and experimental results with 24 DOF biped walking robot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.100-108
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• 2002

A fuzzy logic for collision avoiding navigation of marine vehicles is proposed in this paper. VFF(Virtual Force Field) method, which is used widely in the field of mobile robots, is modifiel to apply to marine vehicles. The method is named MVFF (Modified Virtual Force Field) mothod. The MVFF consists of the determination of the heading angles far track-keeping mode ($\psi_{ca}$)and collision avoidance mode ($\psi_{ca}$). The operator can choose the pattern of the track-keeping mode in the proposed algorithm. The collision avoidance algorithm can handle static and/or moving obstacles. These functons are implemented using fuzzy logic. Various simulation results verify the proposed alogorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.331-336
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• 1998

A systematic design method for PDC(Parallel Distributed Compensation)-type continuous time Takagi-Sugeno(T-S in short) fuzzy control systems which have inaccessible states is developed in this paper. Reduced-dimensional fuzzy state estimator is introduced from existing T-S fuzzy model using the PDC structure of Wang et al. [1] LMI(Linear Matrix Inequalities) problems which represent the stabililty of the reduced-dimensional fuzzy state estimator are derived. Pole placement constraints idea for each rules is adopted to determine the estimator gains and they are also revealed as LMI problems. these LMI problems are combined with Joh et al's [7][8] LMI problems for PDC -type continuous time T-S fuzzy controller design to yield a systematic design method for PDC -type continuous time T-S fuzzy control systems which have inaccessible states.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.259-262
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• 1997

In this paper, a fuzzy model based on the polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. The new algorithm uses PNN algorithm based on Group Method of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy anhd feasibility than other works achieved previously.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.127-137
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• 2006

This paper presents an $H_{\infty}$ controller synthesis method for discrete time fuzzy dynamic systems based on a piecewise smooth Lyapunov function. The basic idea of the proposed approach is to construct controllers for the fuzzy dynamic systems in such a way that a Piecewise smooth Lyapunov function can be used to establish the global stability with $H_{\infty}$ performance of the resulting closed loop fuzzy control systems. It is shown that the control laws can be obtained by solving a set of Bilinear Matrix Inequalities (BMIs). An example is given to illustrate the application of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.442-448
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• 2000

The conventional control of dynamic systems needs accurate mathematical modeling of control systems. But the modeling of dynamic systems require very complex computation process due to complex state equation and many control parameters. Accordingly this paper proposes a state space identification model of the dynamic system using neural networks. The Gauss-Newton method is used to train the proposed neural network and the effectiveness of proposed method is verified through the computer simulation of the Seesaw system identification problem.