• 제어로봇시스템학회논문지
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• 제18권12호
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• pp.1101-1105
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• 2012

This study proposes a method for improvement of PD type fuzzy controller. The method includes self tuner using gradient algorithm that is one of the optimization algorithms. The proposed controller improves simple Takagi-Sugeno type FLC (Fuzzy Logic Control) system. The simple Takagi-Sugeno type FLC system changes nonlinear characteristic to linear parameters of consequent membership function. The simple FLC system could control the system by calibrating parameter of consequent membership function that changes the system response. While the determination on parameter of the simple FLC system works well only partially, the proposed method is needed to determine parameters that work for overall response. The simple FLC system doesn't predict the response characteristics. While the simple FLC system works just like proportional part of PID, our system includes derivative part to predict the next response. The proposed controller is constructed with P part and D part FLC system that characteristic parameter on system response is changed by self tuner for effective response. Since the proposed controller doesn't include integral part, it can't eliminate steady state error. So we include a gain to eliminate the steady state error.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권2호
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• pp.109-114
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• 2002

Various inverted pendulum systems have been frequently used as a model for the performance test of the proposed control system. We first identify a rotary-type inverted pendulum system by the Euler-Lagrange method and then design a FLC (Fuzzy Logic Controller) fur the plant. FLC`s are one of useful control schemes fur plants having difficulties in deriving mathematical models or having performance limitations with conventional linear control schemes. Many FLC`s imitate the concept of conventional PD (Proportional-Derivative) or PI (Proportional-Integral) controller. That is, the error e and the change-of-error are used as antecedent variables and the control input u the change of control input Au is used as its consequent variable for FLC`s. In this paper we design a simple-structured FLC for the rotary inverted pendulum system. We also perform some computer simulations to examine the tracking performance of the closed-loop system.

• Journal of Mechanical Science and Technology
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• 제16권8호
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• pp.1039-1052
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• 2002

This paper presents an application where a Fuzzy-Logic Controller (FLC) is used at a supervisory level to implement mutual coordination of the steering of the two front wheels of a motorcar. The two front wheels are steered by two independent discrete time state feedback controllers with a view to optimize the steering slip angles. The functions of the two controllers are tied together by way of a FLC. Because of the presence of unmodelled dynamics and disturbances acting on the two sides, it is difficult to achieve the desired performance using conventional control systems. This is the primary reason that FLC is emploged to solve the problem. The results show that the implemented system achieved desired coupling between the two independent systems and thereby reduces the difference between the two steered angles.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.93-99
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• 2003

One of the methods to simplify the design process for a fuzzy logic controller (FLC) is to reduce the number of variables representing the rule antecedent. This in turn decreases the number of control rules, membership functions, and scaling factors. For this purpose, we designed a single-input FLC that uses a sole fuzzy input variable. However, it is still deficient in the capability of adapting some varying operating conditions although it provides a simple method for the design of FLC's. We here design two simple-structured adaptive fuzzy logic controllers (SAFLC's) using the concept of the single-input FLC. Linguistic fuzzy control rules are directly incorporated into the controller by a fuzzy basis function. Thus some parameters of the membership functions characterizing the linguistic terms of the fuzzy control rules can be adjusted by an adaptive law. In our controllers, center values of fuzzy sets are directly adjusted by an adaptive law. Two SAFLC's are designed. One of them uses a Hurwitz error dynamics and the other a switching function of the sliding mode control (SMC). We also prove that 1） their closed-loop systems are globally stable in the sense that all signals involved are bounded and 2） their tracking errors converge to zero asymptotically. We perform computer simulations using a nonlinear plant.

• 한국지능시스템학회논문지
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• 제7권4호
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• pp.58-64
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• 1997

FLC(퍼지 제어기 : Fuzzy Logic Controller)는 고전적 제어기보다 외란(disturbance)에 강하고 초기 치의 과도측성(overshoot)이 우수하다. 그리고 미지의 프로세스(process)나 복잡한 시스템의 수학적인 모델링이 불가능한 경우에도 퍼지 추론에 의하여 적절한 제어량을 얻을 수 있다. 그러나 퍼지변수의 양자화 단계 크기에 의해 출력값이 항상 미세한 오차를 가지므로 목표치에 정확히 수럼하지 못한다.[1]. 이 미세한 오차를 제거하기 위한 여러 방법이 [2~4]있지만 본 논문에서는 FLC에 GA(유전알고리즘 : Genetic Algorithm)와 EP(진화프로그래밍 : Evolution programming)를 결합한 GA-FLC, EPFLC Hybrid 제어기를 제안한다. 이 Hybrid 제어기의 츨력 특성과 FLC의 출력 특성을 비교 분석하고, 이 Hybrid 제어기가 오차없이 목표치에 잘 수렴하는 것을 보이고자 한다. 또한 이 두 종류의 Hybrid제어기 수렴 속도 성능도 비교한다.

• 한국정밀공학회지
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• 제11권5호
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• pp.25-32
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• 1994

A nonlinear control algorithm for the depth control of underwater vehicles is presented. In order to consider the deadzone effect of the flow control valve, a nonlinear fuzzy logic controller (FLC) is synthesized and combined with a linear proportional-derivative-acceleration (PDA) controller, which is called the PDA/FLC controller. And to show the effectiveness of the PDA/FLC control system, it is compared with the linear PDA control system through computer simulation. It is found that the PDA/FLC control system is suitable one to maintain the desirable depth of underwater vehicles with deadzone.

• Transactions on Control, Automation and Systems Engineering
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• 제2권1호
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• pp.13-18
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• 2000

In this paper, an Adaptive Evolutionary Computation(AEC) is proposed. AEC uses a genetic algorithm(GA) and an evolution strategy (ES) in an adaptive manner is order to take merits of two different evolutionary computations: global search capability of GA and local search capability of ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. AEC is used to design the membership functions and the scaling factors of fuzzy logic controller (FLC). To evaluate the performances of the proposed FLC, we make an experiment on FLC for the speed control of an actual DC series motor system with nonlinear characteristics. Experimental results show that the proposed controller has better performance than that of PD controller.

• 전기학회논문지P
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• 제67권3호
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.225-230
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• 1992

In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.972-974
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• 1993

This paper presents a new method to automatically design fuzzy logic controller(FLC). The main problems of designing FLC are how to optimally and automatically select the control rules and the parameters of membership function (MF). Cell state space algorithms (CSS), differential competitive learning (DCL) and multialyer neural network are combined in this paper to solve the problems. When the dynamical model of a control process is known. CSS can be used to generate a group of optimal input output pairs(X, Y) used by a controller. The(X, Y) then can be used to determine the FLC rules by DCL and to determine the optimal parameters of MF by DCL and to determine the optimal parameters of MF by multilayer neural network trained by BP algorithm.