• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.64.2-64
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• 2002

Contents 1. Introduction $\textbullet$ Contents 2. System description $\textbullet$ Contents 3. Desired feedback control and function approximation $\textbullet$ Contents 4. Robust adaptive controller design $\textbullet$ Contents 5. Simulation study $\textbullet$ Contents 6. Conclusion

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.408-410
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• 1992

An adaptive hybrid position/force controller for constrained manipulator with uncertain dynamic model parameters and environment stiffness is presented. In this paper, the compliance frame model is constructed by independent positions and forces to be controlled. The adaptive controller based on this compliance frame dynamic model is designed. Lyapunov theory is used for controller design and Stability analysis.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1646_1647
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• 2009

In this paper, Takagi-Sugeno(T-S) fuzzy controller and search method are developed for efficiency optimization of induction motors(IMs). The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of T-S fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is used to control of speed of IMs. Simulation results are presented to validate the proposed controller.

• Proceedings of the KIEE Conference
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• 2009.04a
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• pp.74-76
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• 2009

In this paper, search method and sliding mode observer are developed for efficiency optimization of induction motor. The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. The search controller is based on the "Rosenbrock" method and finds the flux level at the minimum input power of induction motor. Once this optimal flux level has been determined, this information is utilized to update the rule base of a fuzzy controller A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is also used to compensate for mechanical uncertainties in the speed control of induction motor. Simulation results are presented to validate the proposed controller.

• Bulletin of the Korean Chemical Society
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• v.13 no.1
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• pp.1-2
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• 1992

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1777-1782
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• 2011

Brushless DC motors applied in many control systems because of the good respose characteristic and the easy control characteristic. The speed control of the BLDC motors is important in the systems. This paper has designed PSO-PID speed controller for the speed control of BLDC motors. The PSO algorithm optimized the parameters of the PID controller in the PSO-PID speed controller. The several methods obtained the optimal inertia weight of the PSO algorithm by comparison. The optimal inertia weight of the PSO algorithm optimized the PSO-PID speed controller for BLDC motors. This paper confirmed the performance of proposed PSO-PID speed controller through simulation results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.650-655
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• 2012

This paper proposed PSO-Fuzzy controller design method. We could improve the learning performance of fuzzy controller by using PSO algorithm, which had recently showed its robust of performance while solving various difficult optimization problems. In other words, our aim was to forward the controller is performance by deciding fuzzy model structure that had good performance on optimization of the controller, based on PSO. During a simulation, we could see whether the mobile robot could convergence on the final goal or not, and also see the error, and through this process, we found out that this controller is more robust than the conventional controller.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1668-1669
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• 2007

In this paper, we propose nonlinear congestion controller for active queue management of differentiated-services networks. Two important issues in differentiated-services architecture are bandwidth guarantee and fair sharing of unsubscribed bandwidth among TCP flows with and without bandwidth reservation. The nonlinear congestion controller was composed fuzzy logic controller and state feedback controller. The nonlinear congestion controller methodology has been applied to a TCP network. We use NS-2 simulation to demonstrate that the proposed control methodology achieves the desired behavior of the network, and possesses important attributes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.124-133
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• 1996

This paper presents the implementation of variable structure control system for a linear or nonlinear system using neural networks. The overall control system consists of neural network controller and a reaching mode controller. While the former approximates the equivalent control input on the sliding surface, the latter is used to bring the entire system trajectories toward the sliding surface. No supervised learning procedures are needed and the weights of the neural network are tuned on-line automatically. The neural netowrk-based variable structure control system is applied to a nonlinare unstable inverted pendulum system through computer simulations, and implemented using a microcomputer (80486-50MHz) and applied to the DC servomotor position control system. Simulation and experimental results show the expected approximation sliding property is occurred. The proposed controller is compared with a PID controller and shows better performance than the PID controller in abrupt plant parameter change.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.807-809
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• 1995

In this paper, a design of the sliding mode controller using neural networks is proposed. The overall control system consists of a neural network controller and a reaching mode control input. The neural network controller approximates the equivalent control on the sliding surface and reaching mode control input is used to bend the entire system trajectories toward the sliding surface. The proposed controller is applied to the position control of a DC servo motor.