• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.696-698
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• 1998

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. If the parameters of membership function in premise part and consequent part are set adequately, the controller designed can control plant well. But, if the parameters of function are set inadequately, the controller can't control well. So we must modify parameters using adaptive learning procedure. In this paper, we design adaptive fuzzy controller, and then verify its robustness.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.567-574
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• 2006

This paper presents the hardware implementation of a neural network controller for a nonlinear system with a micro-controller unit (MCU) and a field programmable gate array (FPGA) chip. As an on-line learning algorithm of a neural network, the reference compensation technique has been implemented on an MCU, while PID controllers with other functions such as counters and PWM generators are implemented on an FPGA chip. Interface between an MCU and a field programmable gate array (FPGA) chip has been developed to complete hardware implementation of a neural controller. The developed neural control hardware has been tested for balancing the inverted pendulum while controlling a desired trajectory of a cart as a nonlinear system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권1호
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• pp.52-57
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• 2006

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.185-189
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• 1998

This paper addresses analysis and design of a fuzzy model-based-controller for the control of uncertain SISO nonlinear systems. In the design procedure, we represent the nonlinear system by using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller via parallel distributed compensation and sliding mode control. Unlike other parallel distributed controllers, this globally stable fuzzy controller is designed without finding a common positive definite matrix for a set of Lyapunov equations, and has good tracking performance. The stability analysis is conducted not for the fuzzy model but for the real underlying nonlinear system. Furthermore, the proposed method can be applied to partially known uncertain nonlinear systems. A numerical simulation is performed for the control of an inverted pendulum, to show the effectiveness and feasibility of the proposed fuzzy control method.

• 제어로봇시스템학회논문지
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• 제8권11호
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• pp.941-947
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• 2002

Two methods of constructing T-S fuzzy model which is equivalent to a given nonlinear system are presented. The first method is to obtain an equivalent T-S fuzzy model by using the sum of linearly independent scalar functions with constant real matrix coefficients. The sum of products of linearly independent scalar functions is used in the second method. The former method is to formulate the procedures of T-S fuzzy modeling dealt in many examples of previous publications; the latter is a new method. By comparing the number of linearly independent functions used in the two methods, we can easily find out which method makes fewer rules than the other. The nonlinear dynamics of an inverted Pendulum on a cart is used as an equivalent T-5 fuzzy modeling example.

• 한국지능시스템학회논문지
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• 제9권5호
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• pp.496-508
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• 1999

Fuzzy System which are based on membership functions and rules can control nonlinear uncertain complex systems well. However Fuzzy logic controller(FLC) has problems; It is difficult to design the stable FLC and FLC depends mainly on individual experience. Although FLC can be designed using the error back-propagation algorithm it takes long time to converge into global optimal parameters. Well-developed linear system theory should not be replaced by FLC but instead it should be suitably used with FLC. A new methodology is introduced for designing THEN-PART membership functions of FLC based on its well-tuned state feedback controller. A example of inverted pendulum is given for demonstration of the robustness of proposed methodology.

• 한국지능시스템학회논문지
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• 제7권5호
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• pp.60-66
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• 1997

This paper presents a fuzzy inference-based reinforcement learning algorithm of dynamci recurrent neural networks, which is very similar to the psychological learning method of higher animals. By useing the fuzzy inference technique the linguistic and concetional expressions have an effect on the controller's action indirectly, which is shown in human's behavior. The intervlas of fuzzy membership functions are found optimally by genetic algorithms. And using recurrent neural networks composed of dynamic neurons as action-generation networks, past state as well as current state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying it to the inverted pendulum control problem.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2081-2083
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• 2001

In this paper, without mathematical modeling dynamics, the plant parameter in sliding mode are estimated by the indirect adaptive fuzzy control. Adaptive laws for fuzzy parameters and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov stability. The computer simulation results for inverted pendulum system show the performance of the proposed fuzzy sliding mode controller.

• 제어로봇시스템학회논문지
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• 제7권5호
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• pp.393-398
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• 2001

This paper presents a new method with time-varying boundary layer and input gain, variated by Fuzzy Logic Control(FLC) by means of the system state in Sliding Mode Control (SMC). In addition to the time-varying boundary layer, the time-varying range of the fuzzy membership function has an effect on not only chattering reduction but also fast response characteristics. On the basis of SMC with time-varying boundary and FLC with time-varying input and output range, a computer simulation for inverted pendulum results in elimination of the chattering phenomenon and fast response.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1858-1859
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• 2011

제어를 할 때 비선형 시스템을 선형화 하는 것이 중요하다. 선형화를 하기위해는 퍼지 모델을 사용하는데 그 중 바퀴형 역진자 시스템은 비선형 시스템의 파라미터 값을 모두 알아도 T-S퍼지를 기반으로 하여 선형제어를 사용하는데 어려움이 있다. 그래서 Identification을 함으로써 바퀴형 역진자 시스템을 좀 더 편리하게 T-S 퍼지 모델로 만들 수 있다.