• Journal of Welding and Joining
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• v.21 no.4
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• pp.80-86
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• 2003

Arc-spot welding is generally used in joining of precise parts such as case and core in electric compressor. It is important to control joining deformation in electric compressor because clearance control of micrometer order is needed for excellent airtightness and anti-nose. The countermeasures for this deformation in field have mainly been dependent on rule of try and error by operator's experience because of productivities. For control this deformation problem without influence on productivities, development of exact simulation model should be needed. In this study, on the basis of previous study, the analysis model io predict deformation of precise order in arc-spot welded structure with non-uniform stiffness is brought up through feedback and tuning between monitoring data and analysis results. For this, deformation monitoring system was built and boundary condition considering mechanical melting temperature was applied.

• Journal of Welding and Joining
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• v.20 no.4
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• pp.544-550
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• 2002

Arc-spot welding is generally used in joining of precise parts such as case and core in electronic compressor. It is important to control joining deformation in electronic compressor because clearance control in micrometer order is needed for excellent airtightness and anti-nose. The countermeasures far this deformation in field have mainly been dependent on the rule of try and error by operator's experience because of productivities. For control this deformation problem without influence on productivities, development of exact simulation model should be needed. In this study, to solve this deformation problem in arc-spot welded structure with case and core, we intend to make a simulation model that is able to predict deformation in precise order by tuning and feedback between sensing data and simulation results. This paper include development of heat input model for arc-spot welding, temperature monitoring and make a heat transfer model using sensing data in product.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.3
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• pp.7-13
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• 1998

In this paper, the control technique that ID controller are autotuned according to system dynamics, driving out sample in the changeable limits of system dynamics and learning neural network, is presented. In order to lean neural network, the backpropagation learning algorithm is used and the controller parameters obtained by rule-base are used as teacher's values. When load changes, the auto-tuning of PID controller proper to system dynamics is conformed by simulation.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.79-81
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• 1987

Expert systems have a variety of potential applications in process control. The application domain ranges from the entire plant system to a single loop system. Both, off-line and real-time problems may be realized. In this paper, expert system is employed as a part of a single control loop of PID Controller with self-tuning. The goal of expert system in the present study is to build up the necessary process knowledge required for efficient control. In order to achieve this process, the development of an expert system and a prototype model is carried out. OPS5, a rule based production system, is utilized in experiment, and common LISP is used for man-machine interface.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2120-2122
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• 2002

There are several types of control systems that use fuzzy logic controller as a essential system component. The majority of research work on fuzzy PID controller focuses on the conventional two-input PI or PD type controller. However, fuzzy PID controller design is a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this paper we combined conventional PI type and PD type fuzzy controller and set the initial parameters of this controller from the conventional PID controller gains obtained by Ziegler-Nichols tuning or other coarse tuning methods. After that, by replacing some of these parameters with sing1e neurons and making them to be adjusted by back-propagation learning algorithm we designed a neuro-fuzzy controller which showed good performance characteristics in both computer simulation and actual application.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.728-731
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• 1997

This paper proposes the systematic auto-tuning method for fuzzy controller using genetic algorithm(GA). In general, the design of fuzzy logic controller has difficulties in the acquisition of expert's knowledge and relies to a great extent on heuristic knowledge which, in many cases, cannot be objectively justified. So, the performance of the controller can be degraded in the case of plant parameter variations or unpredictable incident which the designer may have ignored. Proposed genetic algorithm searches the optimal rule structure, parameters of membership functions and scaling factors simultaneously and automatically by a new genetic coding format. Inverted pendrum system is provided to show the advantages of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.505-508
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• 2006

This paper presents a new analytical approach for designing multiloop PI controllers for disturbance rejection in multivariable processes with time delay. The proposed method is based on IMC-PID design approach. To overcome a sluggish load response by dominant pole in the process, the IMC filter is modified to compensate the dominant pole effect. Based on the modified IMC filter, an analytical tuning rule for multiloop PI controller is driven by extending the generalized IMC-PID method for single input/single output (SISO) systems [1] to multi input/multi output (MIMO) systems. Simulation results show that the proposed method gives a satisfactory load performance as well as servo performance in the multiloop system.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.682-684
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• 1995

The most effective way to improve the performance of a fuzzy controller may be to optimize look-up values. Look-up values are derived from processes used input-output scale factors, membership functions, rule base, fuzzy inference method and defuzzification. It is powerful way to modify or organize look-up table values. In this paper, We propose the look-up values self-organizing fuzzy controller(LSOFC). We use the plus-minus tuning method(PMTM), scanning values through the processes of addition and subtraction. We show the efficiency of this LSOFC by the results of simulation for nonlinear time-varying plant with unmodelled dynamics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1195-1198
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• 1993

We investigate a systematic design procedure of automated rule generation of fuzzy logic based controller for uncertain dynamic systems such as an engine dynamic model.“Automated Tuning”means autonomous clustering or collection of such meaningful transitional relations in the state-space. Optimal control strategies are included in the design procedures, such as minimum squared error, minimum time, minimum energy or combined performance criteria. Fuzzy feedback control systems designed by the cell-state transition method have the properties of closed-loop stability, robustness under parameter variabtions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller design to a highly nonlinear model of engine idle speed contr l.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2157-2164
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• 2011

In this paper, we introduce a new category of fuzzy inference system based on Type-2 fuzzy c-means clustering algorithm (T2FCM-based FIS). The premise part of the rules of the proposed model is realized with the aid of the scatter partition of input space generated by Type-2 FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we can alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with interval sets. To determine the structure and estimate the values of the parameters of Type-2 FCM-based FIS we consider the successive tuning method with generation-based evolution by means of real-coded genetic algorithms. The proposed model is evaluated with the use of numerical experimentation.