• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.994-997
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• 2003

This Paper presents a study of the performance of a DC servo motor with a model reference adaptive fuzzy speed controller (MRAFSC) in the presences of load disturbances. MRAFSC comprised inner feedback loop consisting of the fuzzy logic controller (FLC) and plant, and outer loop consisting of an adaptation mechanism which is designed for tuning a control rule of the FLC. Experimental results show the good performance in the DC servo motor system with the proposed adaptive fuzzy controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.532-538
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• 1998

In this paper, a new adaptive fuzzy inference method using neural network based fuzzy reasoning is proposed to make a fuzzy logic control system more adaptive and more effective. In most cases, the design of a fuzzy inference system rely on the method in which an expert or a skilled human operator would operate in that special domain. However, if he has not expert knowledge for any nonlinear environment, it is difficult to control in order to optimize. Thus, using the proposed adaptive structure for the fuzzy reasoning system can controled more adaptive and more effective in nonlinear environment for changing input membership functions and output membership functions. The proposed fuzzy inference algorithm is called adaptive neuro-fuzzy control(ANFC). ANFC can adapt a proper membership function for nonlinear plant, based upon a minimum number of rules and an initial approximate membership function. Nonlinear function approximation and rotary inverted pendulum control system ar employed to demonstrate the viability of the proposed ANFC.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.584-588
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• 1991

This paper presents a fuzzy adaptive controller which can improve the control policy automatically. Adaptation is achieved by the addition of on-line identification of the fuzzy inverse model using input-output data pairs of the process. Starting with an initial crude control rule, the adaptive controller matches the model to the process to self-tune the controller. The control algorithm needs much less memory of computer than other SOC algorithms.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.769-772
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• 1993

This paper presents and adaptive fuzzy controller using fuzzy neural networks(FNNs). The adaptive controller uses two FNNs. One FNN is used to identify a fuzzy model of controlled object. The other FNN is used as a fuzzy controller. The fuzzy controller is designed with the linguistic rules of the fuzzy model. The response of the designed control system is checked with a linguistic response analysis proposed by the authors. An adaptive tuning of the control rules of the FNN controller is made possible utilizing the fuzzy model. Simulations using nonlinear controlled objects were done to verify the proposed control system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.44-49
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• 2001

For minimum phase systems, the conventional fuzzy logic controllers (FLCs) use the error and the change-of-error as fuzzy input variables. Then the control rule table is a skew symmetric type, that is, it has UNLP (Upper Negative and Lower Positive) or UPLN property. This property allowed to design a single-input FLC (SFLC) that has many advantages. But its control parameters are not automatically adjusted to the situation of the controlled plant. That is, the adaptability is still deficient. We here design a single-input direct adaptive FLC (SDAFLC). In the AFLC, some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules are adjusted by an adaptive law. The SDAFLC is designed by a stable error dynamics. We prove that its closed-loop system is globally stable in the sense that all signals involved are bounded and its tracking error converges to zero asymptotically. We perform computer simulations using a nonlinear plant and compare the control performance between the SFLC and the SDAFLC.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.95-105
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• 2001

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.89-118
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• 1996

The development and implementation of a real-time, traffic adaptive control scheme based on fuzzy logic through Video Image Detector systems (VIDs) is presented. Through VIDs based image processing, fuzzy logic can be used for a real-time traffic adaptive signal control scheme. Fuzzy control logic allows linguistic and inexact traffic data to be manipulated as a useful tool in designing signal timing plans. The fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategy based on a priori verbal communication. The implementation of fuzzy logic controller for a traffic network is introduced. Comparisons are made between implementations of the fuzzy logic controller and the actuated controller in an isolated intersection. The results obtained from the application of the fuzzy logic controller are also compared with those corresponding to a pretimed controller for the coordinated intersections. Simulation results from the comparisons indicate the performance of the system is between under the fuzzy logic controller. Integration of the aforementioned schemes into and ATMS framework will lead to real-time adjustment of the traffic control signals, resulting in significant reduction in traffic congestion.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.437-440
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• 1998

In this paper, the design method of fuzzy neural network(FNN) controller using indirect adaptive control technique is presented for controlling chaotic nonlinear systems. Firstly, the fuzzy model identified with a FNN in off-line process. Secondly, the trained fuzzy model tunes adaptively the control rules of the FNN controller in on-line process. In order to evaluate the proposed control method, Indirect adaptive control method is applied to the representative continuous-time chaotic nonlinear systems, that is, the Duffing system and the Lorenz system. Simulations are done to verify the effectivencess of controller.