• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.504-506
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• 1999

In this paper, we propose a method which control parametric uncertainty systems using PID controller by fuzzy auto tuning. We get the error and the error change rate of plant output correspond to the initial value of parameter using the Ziegler-Nickols tuning and determine the new proportional gain$(K_p)$ and the integral time $(T_i)$ from fuzzy tuner by the error and error change rate of plant output as a membership function of fuzzy theory. The Fuzzy Auto-tuning algorithm for PID controller operate to adapt variable parameter of plant in parametric uncertainty systems. It is shown this method considerably improve the transient response at computer simulation.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.822-828
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• 2018

AC-DC conversion is a necessary for the systems that require DC source. This conversion has been done via rectifiers based on controlled or uncontrolled semiconductor switches. Advances in the power electronics and microprocessor technologies allowed the use of Pulse Width Modulation (PWM) rectifiers. In this paper, dq-axis current and DC link voltage of three-phase PWM rectifier are controlled by using type-2 fuzzy neural network (T2FNN) controller. For this aim, a simulation model is built by MATLAB/Simulink software. The model is tested under three different operating conditions. The parameters of T2FNN is updated online by using back-propagation algorithm. The results obtained from both T2FNN and Proportional + Integral + Derivate (PID) controller are given for three operating conditions. The results show that three-phase PWM rectifier using T2FNN provides a superior performance under all operating conditions when compared with PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.839-845
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• 2013

A CSTR (Continuous Stirred Tank Reactor) is a highly nonlinear process with varying parameters during operation. Therefore, tuning of the controller and determining the transition policy of controller parameters are required to guarantee the best performance of the CSTR for overall operating regions. In this paper, a methodology employing the 2DOF (Two-Degree-of-Freedom) PID controller, the anti-windup technique and a fuzzy gain scheduler is presented for the temperature control of the CSTR. First, both a local model and an EA (Evolutionary Algorithm) are used to tune the optimal controller parameters at each operating region by minimizing the IAE (Integral of Absolute Error). Then, a set of controller parameters are expressed as functions of the gain scheduling variable. Those functions are implemented using a set of "if-then" fuzzy rules, which is of Sugeno's form. Simulation works for reference tracking, disturbance rejecting and noise rejecting performances show the feasibility of using the proposed method.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.8 no.1
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• pp.53-56
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• 1994

PID controller is being used in many servo control systems. However, when a control system has disturbance or time variable characteristic, it is very difficult to guarantee the robustness of the system. In the way of solving this problem, in this paper, a control method using the PID controller with Fuzzy Logic Regulator is presented. Fuzzy Logic Regulator is designed by error and error change, the kth sampling control input is decided by the addition of the kth sampling defuzzification value and the (k-l)th sampling defuzzification value. Control input is transmitted to input. The robust control function of Fuzzy Logic Regulator is demonstrated by the computer simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.732-735
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• 2007

Recently the control of induction motor for position control has been extensively studied. The representative method is PIDA controller proposed by Jung&Dorf. By designed PIDA controller' parameter had large value. Moreover, this method is very analyze, so that, not adapted controller parameter in disturbance. Besides using generalize fuzzy controller. Because input and output membership function is linguistic type, therefore system response is very slow. So, in this paper we used optimized fuzzy controller. Optimized fuzzy controller is output membership function is unity value. The controller performance was estimated applied to induction motor' position control.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.3
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• pp.245-253
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• 2002

A driver model with nervous neuromuscular system was developed to steer a vehicle along the prescribed path during handling simulations. A 3-dimensional vehicle model with 10 DOF and 3 DOF steering handle are used to perform a computer simulation. PID and fuzzy controller are used to perform single and double lane change, and their tracking abilities were compared. The effects of time delay and preview distance are also investigated, and it is demonstrated that the driver model developed can be an aid far objective evaluation of vehicle handling simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.993-996
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• 2000

A brushless linear motor is suitable for a high-accuracy servo mechanism. It is also suitable for operation with higher speed and precision. Since it does not involve some sort of mechanical coupling, linear driving force can be applied directly. Basic models including magetomotive force and electromotive forces are introduced and simplified. Both conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Several system operational characteristics have been observed. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.973-976
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• 2000

A brushless linear motor is suitalbe fur a high-accuracy servo mechanism. It is also suitable for operation with higher speed and precision. Since it does not involve some sort of mechanical coupling, linear driving force can be applied directly. Basic models including magetomotive farce and electromotive forces are introduced and simplified. Both conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.85-91
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• 2000

This paper proposes a novel method with pseudo-on-line scheme using the optimized look-up table based on the genetic algorithm which does not use the gradient and finds the global optimum of an un-constraint optimization problem. The technique is a pseudo-on-line method that optimally estimates the parameters of fuzzy PID(FPID) controller for systems with non-linearity, using the genetic algorithm. The proposed controller(GFPID) with the auto-tuning function is applied to the on-line and real-time control of speed at 3-phase induction motor, and its computer simulation is carried out. simulation results show that the proposed methodis more excellent that conventional FPID and PID controllers.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.299-303
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• 2000

A brushless linear motor is suitable for operation with higher speed and precision. Since it does not involve mechanical coupling, linear driving force can be applied directly. Conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Several system operational characteristics have been observed. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.