Browsing by Author "Su, SF"

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Evolutionary-based virtual training in extracting fuzzy knowledge for deburring tasks
(2004-03-01)
Su, SF
;
Horng, TJ
;
Young, KY
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this research, the problems of how to teach a robot to execute skilled operations are studied. Human workers usually accumulate their experience after executing the same task repetitively. In the process of training, a worker needs to find ways of adjusting his/her execution. In our system, the parameters for an impedance control scheme are considered as the targets for adjustment in the training process. The way to make adjustments is represented as a set of fuzzy rules in our research. Furthermore, a training scheme, called the evolutionary-based virtual training scheme, is proposed to extract knowledge (a set of fuzzy rules) for robotic deburring tasks. In this approach. an evolutionary algorithm is employed to find the best set of fuzzy rules and a simulation system is built to evaluate the execution performances of candidates. This learning scheme has been applied in finding a set of fuzzy rules that can adjust the parameters of impedance controllers required in deburring operations with satisfactory performance in deburring tasks.
Hybrid compensation control for affine TSK fuzzy control systems
(2004-08-01)
Hsiao, CC
;
Su, SF
;
Lee, TT
;
Chuang, CC
;
電機資訊學士班
The paper proposes a way of designing state feedback controllers for affine Takagi-Sugeno-Kang (TSK) fuzzy models. In the approach, by combining two different control design methodologies, the proposed controller is designed to compensate all rules so that the desired control performance can appear in the overall system. Our approach treats all fuzzy rules as variations of a nominal rule and such variations are individually dealt with in a Lyapunov sense. Previous approaches have proposed a similar idea but the variations are dealt with as a whole in a robust control sense. As a consequence, when fuzzy rules are distributed in a wide range, the stability conditions may not be satisfied. In addition, the control performance of the closed-loop system cannot be anticipated in those approaches. Various examples were conducted in our study to demonstrate the effectiveness of the proposed control design approach. All results illustrate good control performances as desired.
Support vector regression performance analysis and systematic parameter selection
(2005)
Lin, PT
;
Su, SF
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Support Vector Regression (SVR) based on statistical learning is a useful tool for nonlinear regression problems. The SVR method deals with data in a high dimension space by using linear quadratic programming techniques. As a consequence, the regression result has optimal properties. However, if parameters were not properly selected, overfitting and/or underfitting phenomena might occur in SVR. Two parameters sigma, the width of Gaussian kernels and epsilon, the tolerance zone in the cost function are considered in this research. We adopted the concept of the sampling theory into Gaussian Filter to deal with parameter sigma. The idea is to analyze the frequency spectrum of training data and to select a cut-off frequency by including 90% of power in spectrum. The corresponding sigma can then be obtained through the sampling theory. In our simulations, it can be found that good performances are observed when the selected frequency is near the cut-off frequency. For another parameter epsilon, it is a tradeoff between the number of Support Vectors and the RMSE. By introducing the confidence interval concept, a suitable selection of epsilon can be obtained. The idea is to use the L-1-norm (i.e., when epsilon = 0) to estimate the noise distribution of training data. When E is obtained by selecting the 90%, confidence interval, simulations demonstrated superior performance in our illustrative example. By our systematical design, proper values of sigma and epsilon can be obtained and the resultant system performances are nice in all aspects.
Time-optimal control via fuzzy approach
(2004-01-01)
Lin, PT
;
Su, SF
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, a fuzzy model based time-optimal controller will be proposed. The control input is bounded, and only achieved in minimum time. A design approach of the minimal time fuzzy controller using Pontryagin's Minimum Principle (PMP) is presented. The necessary conditions of PMP for time-optimal problem achieve the goal in minimum time under bounded controls can be formulated as bang-bang control. Determination of time-optimal solution by solving the corresponding two-point boundary-value problems (TPBVP) is, in general, done by shooting method. The TPBVP solved by the shooting method that allows to satisfy all the equations and the boundary conditions. An illustrative example is provided to show the validity of the proposed approach.
Time-optimal control via fuzzy approach
(2004-01-01)
Lin, PT
;
Su, SF
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, a fuzzy model based time-optimal controller will be proposed. The control input is bounded, and only achieved in minimum time. A design approach of the minimal time fuzzy controller using Pontryagin's Minimum Principle (PMP) is presented. The necessary conditions of PMP for time-optimal problem achieve the goal in minimum time under bounded controls can be formulated as bang-bang control. Determination of time-optimal solution by solving the corresponding two-point boundary-value problems (TPBVP) is, in general, done by shooting method. The TPBVP solved by the shooting method that allows to satisfy all the equations and the boundary conditions. An illustrative example is provided to show the validity of the proposed approach.