• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.36-51
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• 1995

An approach to robot hybrid position/force control, which allows force manipulations to be realized without overshoot and overdamping while in the presence of unknown environment, is given in this paper. The manin idea is to used dynamic compensation for known robot parts and fuzzy compensation for unknown environment so as to improve system performance. The fuzzy compensation is implemented by using rule based fuzzy approach to identify the unknown environment. The establishment of proposed control system consists of following two stages. First, similar to the resovled acceleration control method, dynamic compensation and PD control based on known robot dynamics, kinematics and estimated environment stiffness is introduced. To avoid overshoot the whole control system is constructed with overdamping. In the second stage, the unknown environment stiffness is identified by using fuzzy reasoning, where the fuzzy compensation rules are obtained priori as the expression of the relationship betweenenvironment stiffness and system. Based on the simulation result, comparison between cases with or without fuzzy identifications are given, which illustrate the improvement achieced.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.58-64
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• 1997

FLC(Fuzzy Logic Controller) is stronger to the disturbance than a classical controller and its overshoot of the intialized value is excellent. In case an unknown process or the mathematical modeling of a complicated system is impossible, a fit control quantity can be acquired by the Fuzzy inference. But FLC can not converge correctly to the desirable value because the FLC's output value by the size of the quantization level of the Fuzzy variable always has a minor error. There are many ways to eliminate the minor error, but I will suggest GA-FLC and EP-FLC Hybrid controller which csombines FLC with GA(Genetic Algorithm) and EP(Evo1ution Programming). In this paper, the output characteristics of this Hybrid controller will be compared and analyzed with those of FLC, it will he showed that this Hybrid controller converge correctly to the desirable value without any error, and !he convergence speed performance of these two kinds of Hyhrid controller also will be compared.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.2
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• pp.135-144
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• 2009

Fuzzy hybrid control technique with a smart tuned mass damper(STMD) was proposed in this study for the suppression of wind-induced motion of a tall building. To develop the effective control algorithm for a STMD, skyhook and groundhook control algorithms were employed. Usually, skyhook controller can effectively reduce STMD motion and groundhook controller shows good control performance for the reduction of building responses. In this study, fuzzy hybrid controller, which can determine an optimal weighting factor for combining two controllers in real time, was developed to improve the control performance of conventional hybrid controller using weighted sum approach. A 76-story office building was used as an example structure to investigate the performance of the proposed controller. A magnetorheological(MR) damper was used to develop a STMD and the control performance of STMD was evaluated comparing with the passive and active TMD. The numerical studies show that the control effectiveness of a STMD is significantly superior to that of the conventional TMD. It is also shown that fuzzy hybrid controller can effectively adjust skyhook and groundhook control algorithms and reduce both responses of STMD and building.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.76-86
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• 2002

In this paper, we suggest a hybrid genetic algorithm reinforced by a fuzzy logic controller (flc-HGA) to overcome weaknesses of conventional genetic algorithms: the problem of parameter fine-tuning, the lack of local search ability, and the convergence speed in searching process. In the proposed flc-HGA, a fuzzy logic controller is used to adaptively regulate the fine-tuning structure of genetic algorithm (GA) parameters and a local search technique is applied to find a better solution in GA loop. In numerical examples, we apply the proposed algorithm to a simple test problem and two complex combinatorial optimization problems. Experiment results show that the proposed algorithm outperforms conventional GAs and heuristics.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.12
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• pp.713-715
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• 2005

The work in this paper presents development of fuzzy logic-based energy management strategy for a fuel cell hybrid electric vehicle. In order for the fuel cell system to overcome the inherent limitation such as slow response time and low fuel economy especially at the low power region, the battery system has come to compensate for the fuel cell system. This type of hybrid configuration has many advantages, however, the energy management strategy between power sources is essentially required. For the optimal power distribution between the fuel cell system and the battery system, a fuzzy logic-based energy management strategy is proposed. In order to show the validity and the robustness of suggested strategy, some simulations are performed for the standard drive cycles.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.67-67
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• 2000

In this paper, a hybrid type fuzzy controller is proposed to maintain molten steel level stable and reliable manner in high speed continuous casting regardless of various disturbances such as casting speed change, tundish weight variation, 치ogging/undoning of SEN(Submerged Entry Nozzle), periodic bulgings, etc. To accomplish this purpose, hardware filter and software filer are carefully designed to eliminate high frequency noise and to smooth input signals from harsh environments. In order to minimize the molten steel level variations from various disturbances the controller uses hybrid type control term: fuzzy logic term, proportional term, differential term and nonlinear feedback compensation tenn. The proposed controller is applied tn commercial mini-mill plant and shows considerable improvement in minimizing the molten steel variation.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.329-338
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• 2012

This paper presents an effective design method for a gas identification system. The design method adopted the sequential combination between the hybrid genetic algorithms and the TSK fuzzy logic system. First, the sensor grouping method by hybrid genetic algorithms led the effective dimensional reduction as well as effective pattern analysis from a large volume of pattern dimensions. Second, the fuzzy identification sub-models allowed handling the uncertainty of the sensor data extensively. By these advantages, the proposed identification model demonstrated high accuracy rates for identifying the five different types of gases; it was confirmed throughout the experimental trials.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.215-219
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• 1998

This paper presents a practical application of fuzzy logic control to temperature control of glass melting furnace. Due to the characteristics of glass melting furnace, a hybrid algorithm of conventional PI controller and fuzzy logic controller is proposed and discussed. Practical implementation results of the production furnace showed the effectiveness of the proposed control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.627-630
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• 2004

The PID type controller has been widely used in industrial application due to its simply control structure, ease of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. This paper presents a hybrid fuzzy logic proportional plus conventional integral derivative controller (fuzzy P+ID). In comparison with a conventional PID controller, only one additional parameter has to be adjusted to tune the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. Finally, the proposed hybrid fuzzy P+ID controller is applied to BLDC motor drive. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.