• Journal of KSNVE
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• v.11 no.1
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• pp.147-155
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• 2001

This paper is concerned with the real-time automatic tuning of the multi-input multi-output positive position feedback controllers for smart structures by the genetic algorithms. The genetic algorithms have proven its effectiveness in searching optimal design parameters without falling into local minimums thus rendering globally optimal solutions. The previous real-time algorithm that tunes a single control parameter is extended to tune more parameters of the MIMO PPF controller. We employ the MIMO PPF controller since it can enhance the damping value of a target mode without affecting other modes if tuned properly. Hence, the traditional positive position feedback controller can be used in adaptive fashion in real time. The final form of the MIMO PPF controller results in the centralized control, thus it involves many parameters. The bounds of the control Parameters are estimated from the theoretical model to guarantee the stability. As in the previous research, the digital MIMO PPF control law is downloaded to the DSP chip and a main program, which runs genetic algorithms in real time, updates the parameters of the controller in real time. The experimental frequency response results show that the MIMO PPF controller tuned by GA gives better performance than the theoretically designed PPF. The time response also shows that the GA tuned MIMO PPF controller can suppress vibrations very well.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.13-14
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• 2008

This paper describes the optimal PSS parameter design for the PSS of EX2000 excitation system. The suggested tuning technique uses the model-based PSS tuning method which have three steps: generation system modeling, determination of PSS parameters, and on-site test. Using this method, the PSS parameters of EX2000 system in Dangjin T/P #4 was designed and verified by linear analysis program, PSS/E, and EMTDC/PSCAD.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.268-273
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• 2005

The conventional PID controller has been widely used in many industrial control system because engineers can easily understand how to deal with three parameters of PID controller. The conventional tuning methods, however, have a tendency depend on experience and experiment. In this paper a real-coded genetic algorithm is used to search for the optimal parameters of PID controller for marine diesel engine. Simulation results compared with conventional PID controller tuning methods show the effectiveness and good performance of proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.442-445
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• 1997

We present a hybrid self-tuning method of fuzzy inference systems with hyper elliptic Gaussian membership functions using genetic algorithm(GA) and back-propagation algorithm. The proposed self-tuning method has two phases : one is the coarse tuning process based on GA and the other is the fine tuning process based on back-propagation. But the parameters which is obtained by a GA are near optimal solutions. In order to solve the problem in GA applications, it uses a back-propagation algorithm, which is one of learning algorithms in neural networks, to finely tune the parameters obtained by a GA. We provide Box-Jenkins time series to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.701-706
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• 1998

A design method of rule -based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of " IF..., THEN,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.251-258
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• 2004

This paper presents the parameter study of multiple tuned liquid damper (MTLCD) applied to the 76-story benchmark building. A parameter study involves the effects of number of TLCD, frequency range, and central tuning frequency ratio, which are important parameters of MTLCD. The performance of MTLCD is carried out numerical analysis which reflects the nonlinear property of liquid motion. The parameters of TLCD exist different each optimal values according to mass ratio. The performance of single-TLCD (STLCD) is sensitive for tuning frequency ratio. Therefore, MTLCD is proposed to protect such the shortcoming of STLCD. The result of numerical analysis presents improved performance for robustness of MTLCD

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1618-1623
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• 2008

In this paper, a new design method for IMC-PID that adds a phase scaling factor of system identifications to the standard IMC-PID controller as a control parameter is proposed. Based on analytically derived frequency properties such as gain and phase margins, this tuning rule is an optimal control method determining the optimum values of controlling factors to minimize the cost function, integral error criterion of the step response in time domain, in the constraints of design parameters to guarantee qualified frequency design specifications. The proposed controller improves existing single-parameter design methods of IMC-PID in the inflexibility problem to be able to consider various design specifications. Its effectiveness is examined by a simulation example, where a comparison of the performances obtained with the proposed tuning rule and with other common tuning rules is shown.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.1
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• pp.116-123
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• 2005

The PID controllers have been widely accepted in many industrial systems due to their robust performance in a wide range of operating conditions and their functional simplicity To implement a PID controller, its three parameters must be determined for the given plant. Conventional tuning methods are mainly based on experience and experiment and are lack of systematic procedure Recently. to overcome drawbacks of conventional tuning methods, genetic algorithms have been used, In this paper a real-coded genetic algorithm is employed to search for the optimal parameters of the PID controller for speed control of marine gas turbine engines. Simulation results show the effectiveness of the proposed scheme.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.87-88
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• 2006

In this paper, tuning rules of the PID controller for load disturbance rejection are proposed incorporating with real-coded genetic algorithms(RCGAs). The optimal parameters sets of the PID controller are obtained based on a first-order plus time delay model and a RCGA. As for assessing the performance of the controller, criteria(ISE, IAE and ITAE) are adopted. Then tuning formulae are derived using the tuned parameters sets, potential tuning rule models and another RCGA. A simulation work is carried out to verify the effectiveness of the proposed rules.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.644-649
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• 2006

In this paper, a methodology for estimating the model parameters of a discrete-time system and tuning a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems regarding parameter estimation and controller tuning, pseudo-parallel genetic algorithms(PPGAs) are used. The parameters of a discrete-time system are estimated using both the model adjustment technique and a PPGA. The digital PID controller is described by the pulse transfer function and then its three gains are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.