http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
정병묵,Chung, Byeong-Mook 대한기계학회 1996 大韓機械學會論文集A Vol.20 No.2
A fuzzy learning algorithm to get the optimal fuzzy rules is presented in this paper. The algorithm introduces a reference model to generate a desired output and a performance index funtion instead of the performance index table. The performance index funtion is a cost function based on the error and error-rate between the reference and plant output. The cost function is minimized by a gradient method and the control input is also updated. In this case, the control rules which generate the desired response can be obtained by changing the portion of the error-rate in the cost funtion. In SISO(Single-Input Single- Output)plant, only by the learning delay, it is possible to experss the plant model and to get the desired control rules. In the long run, this algorithm gives us the good control rules with a minimal amount of prior informaiton about the environment.
정병묵(Byeong Mook Chung),여인주(In Joo Yeo),고태조(Tae Jo Ko),이천(Cheon Lee) Korean Society for Precision Engineering 2008 한국정밀공학회지 Vol.25 No.7
High precision machining technology has become one of the important parts in the development of a precision machine. Such a machine requires high speed on a large workspace as well as high precision positioning. For machining systems having a long stroke with ultra precision, a dual-stage system including a global stage (coarse stage) and a micro stage (fine stage) is designed in this paper. Though linear motors have a long stroke and high precision feed drivers, they have some limitations for submicron positioning. Piezo-actuators with high precision also have severe disadvantage for the travel range, and the stroke is limited to a few microns. In the milling experiments, the positional accuracy has been readily achieved within 0.2 micron over the typical 20 ㎜ stroke, and the path error over 2 micron was reduced within 0.2 micron. Therefore, this technique can be applied to develop high precision positioning and machining in the micro manufacturing and machining system.
동적 마찰이 있는 다변수 시스템에서의 PID 학습 제어
정병묵(Byeong Mook Chung) Korean Society for Precision Engineering 2007 한국정밀공학회지 Vol.24 No.12
There have been many researches for optimal controllers in multivariable systems, and they generally use accurate linear models of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. Therefore, it is necessary a PID gain tuning method without explicit modeling for the multivariable plant dynamics. The PID tuning method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the error-related objective function. This paper, especially, focuses on the role of I-controller when there is a steady state error. However, it is not easy to tune I-gain unlike P- and D-gain because I-controller is mainly operated in the steady state. Simulations for an overhead crane system with dynamic friction show that the proposed PID-LC algorithm improves controller performance, even in the steady state error.
정병묵(Byeong Mook Chung) Korean Society for Precision Engineering 2006 한국정밀공학회지 Vol.23 No.9
The synthesis of optimal controllers for multivariable systems usually requires an accurate linear model of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. This paper presents a novel learning method for the synthesis of LQR controllers that doesn't require explicit modeling of the plant dynamics. This method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the LQR objective function. It becomes easier and more convenient because it is relatively very easy to get the sign of Jacobian instead of its Jacobian. Simulations involving an overhead crane and a hydrofoil catamaran show that the proposed LQR-LC algorithm improves controller performance, even when the Jacobian information is estimated from input-output data.