• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1142-1144
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• 2003

This Paper presents a speed controller based on the adaptive fuzzy tuning method for brushless DC(BLDC) motor drives under load variations. Generally, the speed tracking control systems use PI controller due to its simple structure and easy of design. PI controller, however, suffers from the electrical machine parameter variations and disturbances. In order to improve the tracking control performance under load variations, PI controller of which the parameters are modified during operation by adaptive fuzzy tuning method. This method based on optimal fuzzy logic system has simple structure and computational simplicity. It needs only sample data which is obtained by optimal controller off-line. As the sample data implemented in the adaptive fuzzy system can be modified or extended, a flexible control system can be obtained. Simulation results show the usefulness of the proposed controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.40-44
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• 2002

In this paper, robust control method using fuzzy PI parameter tuning is proposed to control constant thrust force on load variation. First, a structure and thrust force equations of the LPM are described. Second, an controller with PI parameter-tuning using a fuzzy theory is proposed to achieve high-precision position with constant thrust force of the LPM. Finally, the effectiveness of an fuzzy PI controller is demonstrated by some simulated and experimental results. Accurate tracking response and superior dynamic performance can be obtained due to the powerful on-line Fuzzy PI gain tuning method with regard parametric variations and load thrust force variations.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2126-2128
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• 2001

This paper suggests that the immune network algorithm based on fuzzy set can effectively be used in tuning of a PID controller for multivariable process or nonlinear process. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that from a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. Along with these, this paper used the fuzzy set in order that the stimulation and suppression relationship between antibody and antigen can be more adaptable controlled against the external condition, including noise or disturbance of plant. The immune network based on fuzzy set suggested here is applied for the PID controller tuning of multivariable process with two inputs and one output and is simulated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.188-199
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• 2014

Although the fuzzy logic controller is superior to the proportional integral derivative (PID) controller in motor control, the gain tuning of the fuzzy logic controller is more complicated than that of the PID controller. Using mathematical analysis of the proportional derivative (PD) and fuzzy logic controller, this study proposed a design method of a fuzzy logic controller that has the same characteristics as the PD controller in the beginning. Then a design method of a fuzzy logic controller was proposed that has superior performance to the PD controller. This fuzzy logic controller was designed by changing the envelope of the input of the of the fuzzy logic controller to nonlinear, because the fuzzy logic controller has more degree of freedom to select the control gain than the PD controller. By designing the fuzzy logic controller using the proposed method, it simplified the design of fuzzy logic controller, and it simplified the comparison of these two controllers.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.182-186
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• 1992

A self tuning technique is derived for PID controllers which are widely used in industries. The tuning algorithm is based upon a fuzzy indirect reasoning method and an iterative technique. The fuzzy technique is considered to obtain ease and simplicity of tuning process. The PID gains for the first tuning action are determined by a method which is modified from the Ziegler-Nichols step response method. The first PID gains are determined to obtain a control performance so close to a design performance that the followed tuning process can be made effectively. The design parameters are given as time-domain variables which human is familiar with. The results of simulation studies show that the proped tuning method can produce an effective tuning for arbitaray design performances.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.541-543
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• 1998

Generally, PI control is simple and easy to implement and gains of PI control are determined by specifying a dynamics of the servo driver system. However, the gain-tuning is so difficult that it is relied on an expert's effort. This paper presents a gain auto-tuning method for PI controllers based on a fuzzy inference mechanism. First, the proposed fuzzy inference system identifies a system moment of inertia and adjusts control gains by using the difference in speed responses between a real plant and a reference model. Second, this paper proposes an improved fuzzy PI controller. To reduce the speed overshoot, we adapt a control method that selects a proper PI gains with respect to the load inertia variation. To prove the validity of the proposed gain tuning algorithm and the feasibility of the servo drive, a high performance servo drive will be implemented by DSP(TMS320C31) and intelligent power module (IPM). The proposed controller is applied to the speed control of the 300W AC servo motor. Some simulations and experimental results show that the proposed fuzzy PI controller is more robust than the conventional PI controller against the load inertia variation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.398-403
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• 1993

A rule based fuzzy expert system to self-tune PID controllers is proposed in this paper. The proposed expert system contains two rule bases, where one is responsible for "Long term tuning" and the other for "Incremental tuning". The rule for "Long term tuning" are extracted from the Wills'map and the knowledge about the implicit relations between PID gains and important long term features of the output response such as overshoot, damping and rise time, etc., while 'Incremental tuning" rules are obtained from the relations between PID gains and short term features, error and change in error. In the PID control environment, the proposed expert system operates in two phases sequentially. In the first phase, the long term tuning is performed until long term features meet their desired values approximately. Then the incremental tuning tarts with PID gains provided by the long term tuning procedure. It is noticeable that the final PID gains obtained in the incremental tuning phase are only the temporal ones. Simulation results show that the proposed rule base for "Long term tuning" provides superior control performance to that of Litt and that further improvement of control performance is obtained by the "Incremental tuning'.ance is obtained by the "Incremental tuning'.ing'.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.479-483
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• 1992

This paper defines the relationship between PI type fuzzy control system and conventional PI control system, and discusses the relationship of parameters and control action in fuzzy controller. The tuning algorithm that updates ouput variable scaling factor of fuzzy controller is proposed .The proposed sheme is applied to the simulations of 2 selected dynamical plants. The simulation results show that the controller is effective in controlling dynamical plants.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.72-72
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• 2000

This paper presents a methodology combining sliding mode control and fuzzy control to tune the boundary layer and input gain according to the system state. The equivalent control is designed such that the nominal system exhibits desirable dynamics, The robust control with fuzzy self-tuning is then developed to guarantee the reaching condition and reduce chattering phenomenon in the presence of parameter and disturbance uncertainties.