• Journal of the Korean Society of Industry Convergence
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• v.19 no.3
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• pp.101-108
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• 2016

I propose a hybrid fuzzy controller with a look-up table to improve the performance of the FARMA(Fuzzy Auto-regressive Moving Average) fuzzy controller. The hybrid structure of the proposed method is composed of a fuzzy controller with a look-up table of the PD type and the FARMA fuzzy controller. The proposed method improves poor performance due to the lack of I/O data to calculate predictive output and shows robust performance over the FARMA fuzzy controller when a incorrect Dmax value is selected by trial and error. I executed some computer simulations on the regulation problem of an inverted pendulum system and compared the results with those of the FARMA fuzzy controller.

• Journal of Drive and Control
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• v.15 no.1
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• pp.28-37
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• 2018

When the wind speed rises above the rated wind speed, the produced power of the wind turbines exceeds the rated power. Even more, the excessive power results in the undesirable mechanical load and fatigue. A solution to this problem is pitch control of the wind turbines. This paper presents a systematic design method of a collective pitch controller for the wind turbines using a discrete fuzzy Proportional-Integral (PI) controller. Unlike conventional PI controllers, the fuzzy PI controller has variable gains according to its input variables. Generally, tuning the parameters of fuzzy PI controller is complex due to the presence of too many parameters strongly coupled. In this paper, a systematic method for the fuzzy PI controller is presented. First, we show the fact that the fuzzy PI controller is a superset of the PI controller in the discrete-time domain and the initial parameters of the fuzzy PI controller is selected by using this relationship. Second, for simplicity of the design, we use only four rules to construct nonlinear fuzzy control surface. The tuning parameters of the proposed fuzzy PI controller are also obtained by the aforementioned relationship between the PI controller and the fuzzy PI controller. As a result, unlike the PI controller, the proposed fuzzy PI controller has variable gains which allow the pitch control system to operate in broader operating regions. The effectiveness of the proposed controller is verified with computer simulations using FAST, a NREL's primary computer-aided engineering tool for horizontal axis wind turbines.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.45.1-45
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• 2001

This paper proposes a Mamdani fuzzy PID controller for controlling a process with small dead time. The controller composes of a parallel structure of fuzzy PI controller and fuzzy PD controller. Each controller has two inputs, error and change of error. Hence, the control signal of the proposed controller is the average value of the output of the fuzzy PI and PD controllers. The Mamdani fuzzy PID controller is easily to be adjusted to meet the desired control system performances both in transient state and steady state. The simulation results of the proposed Mamdani fuzzy PID controller by using the same parameters (proportional gain, integral time and derivative time) as the conventional PID controller are shown. The response of the Mamdani fuzzy PID control system is faster than the conventional PID control system. Both system responses have ...

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.221-224
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• 2000

We consider one of robust controller, fuzzy-sliding mode controller dealing with model uncertainty, simplified representation of nonlinear system, changed parameters of plant. We propose fuzzy-sliding mode algorithm which provides control input that has system states approaching the choosed sliding surface. This fuzzy controller has a rule base to get initial states converged on sliding surface. This algorithm Is applied to a transfer function of DC motor to be modeled simply and do position control of DC motor due to system parameters. We compare fuzzy-sliding mode controller to both sliding mode controller and fuzzy controller to identify roust control.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.5
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• pp.550-556
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• 2014

In this study, for the control of a single water tank system, a fuzzy-PID controller design technique based on a fuzzy model is investigated. For this purpose, a water tank system is linearized as a number of submodels depending on the operating point, and a fuzzy model is obtained by fuzzy combining. Each submodel is approximated as a first order time delay model, and a PID controller is designed using several existing tuning techniques. Then, through the fuzzy combination of this controller using the same method as that of the fuzzy model, a fuzzy-PID controller is designed. For the proposed technique, a simulation is performed using the fuzzy model of a water tank system, and the validity is examined by comparing its performance with that of a PID controller.

• Journal of the Korean Society of Safety
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• v.17 no.4
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• pp.87-93
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• 2002

This paper is presented new fuzzy controller for high performance of induction motor drive. New fuzzy controller took out appropriate amounts of accumulated control input according to fuzzy described situations in addition to the incremental control input calculated by conventional direct fuzzy controller. The structures of the proposed controller was motivated by the problems of direct fuzzy controller. The direct controller was given generally inevitable overshoot when one tries to reduce rise time of response. The undesirable characteristics of the direct fuzzy controller are caused by integrating operation of the controller, even though the integrator itself is introduced to overcome steady state error in provide a thorough comparative insight into the behavior of induction motor drive with direct and new fuzzy speed controller. The validity of the comparative results is confirmed by the simulation results for induction motor drive system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.87-97
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• 1993

To improve the limitation of fuzzy PI controller when is applied to systems of order higher than one, a fuzzy PI controller that fuzzily resets or amplifies the accumulated control input according to fuzzy rules defined on (error, change of error) space is proposed. The proposed controller structure was motivated by the characteristics of fuzzy PI controller that it generally gives unevitable large overshoot in trial of reducing rise time of response especially when a system of order higher than one is considered. Based on the observation that the undesirable characteristics of the fuzzy PI controller is caused by integrating control input excessively, even though the integrator is introduced to overcome steady state error, we propose a controller that clear out or doubles integrated control input in a fuzzy manner according to the situation to reduce rise time as well as overshoot. To show the usefulness of the proposed controller, it is applied to the systems that are difficult to stabilize or difficult to get satisfactory response by conventional fuzzy PI controllers.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.3
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• pp.8-13
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• 2007

A hybrid fuzzy PID controller for a pneumatic chamber is proposed in this paper. First, a mathematical model of a pneumatic pressure servocontrol system was developed where separate implementations of a PID controller and a fuzzy controller were made. The experimental results using a step input signal revealed that the PID controller accurately controlled the steady-state pressure but did not robustly handle parameter variations in the system while the fuzzy controller provided a fast rise time and low overshoot of the pressure in the system. In order to attain the advantages of both the fuzzy and PID controllers, a hybrid control scheme was developed. The experimental results show that the hybrid fuzzy PID controller proposed in this study does indeed possess the advantages of both PID and fuzzy controllers. Hence, it can be concluded that the hybrid fuzzy PID controller is suited for high-precision control of pressure in a pneumatic chamber.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1997.03a
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• pp.235-246
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• 1997

Hybrid fuzzy gain scheduling controller is composed of a PD control and a fuzzy control for taking the advantage of each scheme. The key structure of the hybrid fuzzy gain scheduling control scheme is so called a switch which calculates weighting values between the fuzzy controller and the PD controller. However, due to the requirement of the switch , the hybrid fuzzy gain scheduling control scheme needs extra fuzzy logic processing, thus the structure is complicated. and requires more calculation time. To eliminate the drawbacks, a new hybrid fuzzy gain scheduling control scheme is proposed in this paper. In the proposed scheme, the membership function, for calculating of weithting value, and the input and output membership functions are combined. Thus the proposed hybrid scheme does not require switch for calculation of weighting value, and as a result, the calculation time is faster and the structure is more simple than the existing hybrid controller. Computer simulation results for an inverted pendulum model under Pole-Placement PID controller, fuzzy gain scheduling controller,existing hybrid controller , and proposed hybrid controller are compared to demonstrate the good property of the proposed hybrid controller.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.3
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• pp.30-37
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• 2004

This thesis develops a fuzzy-PID control algorithm for control the filament winding tension. It is developed by applying classical PID control technique to a fuzzy logic controller. It is composed of a fuzzy-PI controller and a fuzzy-D controller. The fuzzy-PI controller uses error and integrated error as inputs, and the fuzzy-D controller uses derivative of error as input. The fuzzy-PI controller uses Takagi-Sugeno fuzzy inference system, and the fuzzy-D controller uses Mamdani fuzzy inference system. The fuzzy rule base for the fuzzy-PI controller is designed using 19 rules, and the fuzzy rule base for the fuzzy-D controller is designed using 5 rules. A test-bed is set-up for verifying the effectiveness of the developing control algorithm in control the filament winding tension. It is composed of a mandrel, a carriage, a force sensor, a driving roller, nip rollers, a creel, and a real-time control system. Nip rollers apply a vertical force to a filament, and the driving roller drives it. The real-time control system is developed by using MATLAB/xPC Target. First, experiments for showing the inherent problems of an open-loop control scheme in a filament winding are performed. Then, experiments for showing the robustness of the developing fuzzy-PID control algorithm are performed under various working conditions occurring in a filament winding such as mandrel rotating speed change, carriage traversing, spool radius change, and reference input change.