• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.414-416
• /
• 1998

One significant error in XY table is due to friction and disturbance. However, the characteristics of this friction is not easy to predict and analyze because of its nonlinearity. Therefore, it is difficult for conventional controller to compensate it effectively. In order to solve this problem, this paper presents a position controller based on fuzzy logic controller(FLC) that is suitable for system with unknown and unmodelled dynamics. The performance of the proposed controller are demonstrated by simulation results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.191-196
• /
• 1998

A new Fuzzy Logic Controller(FLC) called a Gradient-Descent Based Self-Organizing Controller is presented. The Self-Organizing Controller(SOC) has two inputs such as error and change of error, and updates control rules with monitoring a performance measure. There are many works in the SOC which concentrate on the self-organizing ability in control rule base, but have a few research on the performance measure which is akin to sliding mode control. With this procedure, we can get a robust performance measure on the SOC. To verify the perfomance of proposed controller, we have performed for the cart-pole system which is one of the well-known benchmark problem in the control literature.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.5
• /
• pp.2170-2178
• /
• 2015

This paper presents the effective and reliable speed control of PMDC motors under variable loads and reference speeds. As is known DC motors are more preferred in industrial practices. This is that, the PMDC motors don’t require brush and commutator care and to increase in torque per motor depending on developments in power electronics. In this study, proportional-integral controller (PI) and fuzzy logic controller (FL) have been designed for speed control of PMDC motor. In the design of these controllers, characteristics such as minimum overrun time, response time to the load, settling time and ideal rise time have been taken into consideration for better stability performance. In this design, the best system response was searched by examining the effect of different defuzzification methods onto the fuzzy logic system response. In conclusion, it has been seen that FL controller has a better performance for variable speed-load control of PMDC motor compared to PI controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.25 no.9
• /
• pp.33-43
• /
• 2011

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• Proceedings of the KIEE Conference
• /
• 1999.07g
• /
• pp.3045-3047
• /
• 1999

This paper deals with a single flexible link robot system using two fuzzy logic controllers(FLC). The one is used for controlling the rigid position of the beam while it is rotated from one position to another. The other is adopted to reduce the oscillation caused by the rigid body motion. Many simulations are carried out to investigate characteristics of the controlled system. There are good results compared with other systems using PD controller. And also the system could be exactly controlled by the proper setting conditions for FLC.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.2
• /
• pp.203-208
• /
• 1998

This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.8
• /
• pp.1405-1411
• /
• 2007

In this paper, a new speed sensorless control based on a fuzzy compensator are proposed for the interior permanent magnet synchronous motor (IPMSM) drives. The conventional proportional plus integrate(PI) control are very sensitive to step change of the command speed, parameter variations and load disturbance. To cope with these problems of the PI control, the estimated speeds are compensated by using the fuzzy logic controller (FLC). In the FLC used by the speed compensator of the IPMSM, the system control parameters are adjusted by the fuzzy rule based system, which is a logical model of the human behavior for process control. The effectiveness of algorithm is confirmed by the experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.956-959
• /
• 1993

We propose a new PI FLC which utilizes the error(e), change of error( e), and previous control(u(k-1)). It is shown by simulation that the proposed scheme gives better performance in steady state than the conventional PI FLC.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1996.10a
• /
• pp.182-186
• /
• 1996

Control of flexible spacecraft is investigated. GA(Genetic Algorithm) based Fuzzy Logic Controller is designed to implement for the attitude control of flexible satellite. The results obtained by employing GA based FLC are compared with those by FLC. It shows much shorter settling time and smaller tip mass oscillation.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.164.2-164
• /
• 2001

Sliding mode control (SMC) with variable nonlinear boundary layer is proposed. Two Fuzzy logic controllers (FLCs) are used to decide both boundary layer thickness and nonlinear interpolation using sigmoid function in the boundary layer. The nonlinear interpolation in the boundary layer suing FLC reduces stead state error and chattering. Sigmoid function is used to nonlinear interpolation in the boundary layer sigmoid function parameter with FLC. To demonstrate its performance, the Proposed control algorithm is applied to a simple nonlinear system.