• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.972-980
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• 2010

In this study, we introduce the design methodology of an optimized type-2 fuzzy cascade controller with the aid of hierarchical fair competition-based genetic algorithm(HFCGA) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. Consequently the displacement change of the position of the moving ball and its ensuing change of the angle of the beam results in the change of the position angle of a servo motor. The type-2 fuzzy cascade controller scheme consists of the outer controller and the inner controller as two cascaded fuzzy controllers. In type-2 fuzzy logic controller(FLC) as the expanded type of type-1 fuzzy logic controller(FLC), we can effectively improve the control characteristic by using the footprint of uncertainty(FOU) of membership function. The control parameters(scaling factors) of each fuzzy controller using HFCGA which is a kind of parallel genetic algorithms(PGAs). HFCGA helps alleviate the premature convergence being generated in conventional genetic algorithms(GAs). We estimated controller characteristic parameters of optimized type-2 fuzzy cascade controller applied ball & beam system such as maximum overshoot, delay time, rise time, settling time and steady-state error. For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on HFCGA, is presented in comparison with the conventional PD cascade controller based on serial genetic algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1624-1632
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• 2009

In this study, we introduce the design methodology of an optimized Interval Type-2 fuzzy controller. The fixed MF design of type-1 based FLC leads to the difficulty of rule-based control design for representing the linguistically uncertain expression. In the Type-2 FLC as the expanded type of Type-1 FLC, we can effectively improve the control characteristic by using the footprint of uncertainty(FOU) of membership function. Type-2 FLC has a robust characteristic in the unknown system with unspecific noise when compared with Type-1 FLC. Through computer simulation as well as practical experiment, we compare their performance by applying both the optimized Type-1 and Type-2 fuzzy cascade controllers to ball and beam system. To evaluate each controller performance, we consider controller characteristic parameters such as maximum overshoot, delay time, rise time, settling time and steady-state error.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.179.5-179
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• 2001

Generally, while PD-type fuzzy controller has good performance in transient period, it has uniform steady state error of response. To improve limitations of PD-type fuzzy controller, we propose a new fuzzy controller to improve the performance of transient response and to eliminate the steady state error of response. In this paper, PD-type fuzzy controller is used a simplified indirect inference method(SIIM). When the SIIM is applied, the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. The outputs of this controller are the output calculated by PD-type fuzzy controller and the accumulated error scaling factor. Here, the accumulated error scaling factor is adjusted by fuzzy rule according to the system state variables. To show the usefulness of the proposed controller, it is applied to 0-type 2nd-order linear system.

• Smart Structures and Systems
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• v.31 no.2
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• pp.199-212
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• 2023

Nowadays fuzzy logic in control applications is a well-recognized alternative, and this is thanks to its inherent advantages. Generalized type-2 fuzzy sets allow for a third dimension to capture higher order uncertainty and therefore offer a very powerful model for uncertainty handling in real world applications. With the recent advances that allowed the performance of general type-2 fuzzy logic controllers to increase, it is now expected to see the widespread of type-2 fuzzy logic controllers to many challenging applications in particular in problems of structural control, that is the case study in this paper. It should be highlighted that this is the first application of general type-2 fuzzy approach in civil structures. In the following, general type-2 fuzzy logic controller (GT2FLC) will be used for active control of a 9-story nonlinear benchmark building. The design of type-1 and interval type-2 fuzzy logic controllers is also considered for the purpose of comparison with the GT2FLC. The performance of the controller is validated through the computer simulation on MATLAB. It is demonstrated that extra design degrees of freedom achieved by GT2FLC, allow a greater potential to better model and handle the uncertainties involved in the nature of earthquakes and control systems. GT2FLC outperforms successfully a control system that uses T1 and IT2 FLCs.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1879_1880
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• 2009

본 논문에서는 제어상황에 따라 Type-1 Fuzzy Logic Controller가 선택적으로 rule을 사용하도록 rule 선택 알고리즘을 제안 한다. 그리고 이를 통해 연산 효율성을 높이는 방법에 관해 논한다. Type-1 Fuzzy Logic Controller는 기존의 제어기에 비해 설계하기 쉽고 성능이 더 뛰어나다. 그러나 제어 변수가 많아질수록 rule의 개수가 늘어나 연산량이 증가하게 된다. 연산량이 많아지면 고성능의 컴퓨터에서는 실시간 연산에 문제가 없으나 산업용 micro controller에서는 실시간 연산을 구현하는데 한계가 발생한다. 본 논문에서는 Type-1 Fuzzy Logic System의 논리구조에 근거하여 Type-1 Fuzzy Logic Controller의 연산량을 감소시킬 수 있는 알고리즘을 제안한다. 제안한 알고리즘은 제어상황에 따라 필요한 rule들만 선택적으로 제어값 도출을 위한 연산에 관여하도록 한다. Matlab 시뮬레이션을 통해 제안한 알고리즘의 유용성과 연산량을 실험하였다. 실험대상은 2륜 이동로봇으로 하였고 step 응답과 전/후진 시 결과를 관찰하였다. 실험 결과 제안한 알고리즘이 기존의 Type-1 Fuzzy Logic Controller에 비해 제어상황에 따라 필요한 rule들만 선택적으로 사용하는 것을 확인하였다. 결과적으로 연산 효율성이 향상되었다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.68-68
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• 2000

This paper presents a fuzzy modified PID controller that uses linear fuzzy inference method. In this structure, the proportional and derivative gains vary with the output of the system under control. 2-input PD type fuzzy controller is designed to obtain the varying gains. The proposed fuzzy PID structure maintains the same performance as the general-purpose linear PID controller, and enhances the tracking performance over a wide range of input. Numerical simulations and experimental results show the effectiveness of the fuzzy PID controller in comparison with the conventional PID controller.

• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.226-231
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• 2016

Researching and developing mobile robot are quite important. Autonomous driving of mobile robot is important in various working environment. For its autonomous driving, mobile robot detects obstacles and avoids them. Purpose of this thesis is to analyze kinematics model of the mobile robot and show the efficiency of type-2 fuzzy self-tuning PID controller used for controling steering angle. Type-2 fuzzy is more flexible in verbal expression than type-1 fuzzy because it has multiple values unlike previous one. To compare these two controllers, this paper conduct a simulation by using MATLAB Simulink. The result shows the capability of type-2 fuzzy self-tuning PID is effective.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.3
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• pp.193-198
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• 2002

In this paper, we Propose a PD+I fuzzy controller using an error-accumulating applying factor. In fuzzy control, analytical study was done formerly, in which fuzzy control can be classified by PD type and PI type, and also the study for getting merits of both types was done, too. But the mixed type has a complex structure and many parameters. The proposed fuzzy controller is 2-input 2-out-put and PD type fuzzy control is used as a basic structure. And the proposed controller annihilates a steady-state error and improves transient responses because of using the error-accumulating applying factor which is determined in the real time along the current state of controlled process. Futhermore it is easy to tune the system because of decreasing the number of scaling factors and the I type controller with resetting resolves the integral wind-up problem. Finally we apply the proposed scheme to various plants and show the performance betterment.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.2
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• pp.172-182
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• 2013

This paper focuses on the modeling and intelligent control of the new Eight-Rotor MAV, which is used to solve the problem of the low coefficient proportion between lift and gravity for the Quadrotor MAV. The Eight-Rotor MAV is a nonlinear plant, so that it is difficult to obtain stable control, due to uncertainties. The purpose of this paper is to propose a robust, stable attitude control strategy for the Eight-Rotor MAV, to accommodate system uncertainties, variations, and external disturbances. First, an interval type-II fuzzy neural network is employed to approximate the nonlinearity function and uncertainty functions in the dynamic model of the Eight-Rotor MAV. Then, the parameters of the interval type-II fuzzy neural network and gain of sliding mode control can be tuned on-line by adaptive laws based on the Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. The validity of the proposed control method has been verified in the Eight-Rotor MAV through real-time experiments. The experimental results show that the performance of the interval type-II fuzzy neural network based adaptive sliding mode controller could guarantee the Eight-Rotor MAV control system good performances under uncertainties, variations, and external disturbances. This controller is significantly improved, compared with the conventional adaptive sliding mode controller, and the type-I fuzzy neural network based sliding mode controller.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.