Fuzzy continuous gain scheduling H/sub ∞/ control based on Taylor series fitting for robotic manipulators

2003 ◽  
Author(s):  
Zhongwei Yu ◽  
Huitang Chen ◽  
Peng-Yung Woo
Keyword(s):  
Taylor Series ◽  
Robotic Manipulators ◽  
Gain Scheduling ◽  
Continuous Gain Scheduling

Fuzzy continuous gain scheduled H∞ control based on Taylor series fitting for robotic manipulators

Robotica ◽  
2002 ◽  
Vol 20 (4) ◽  
pp. 451-462
Author(s):  
Zhongwei Yu ◽  
Huitang Chen ◽  
Peng-Yung Woo
Keyword(s):  
Taylor Series ◽  
Dynamic Performance ◽  
Robotic Manipulators ◽  
Fast Response ◽  
Linear Matrix ◽  
Fast State ◽  
Lmi Approach ◽  
New Approach ◽  
System States ◽  
Gain Scheduled

A new approach to the design of a fuzzy continuous gain scheduled H∞ controller based on Taylor series fitting for n-joint rigid robotic manipulators, which guarantees satisfactory dynamic characteristics in the whole movement range, is presented. This scheme combines the gain scheduled H∞ theory with the Linear Matrix Inequalities (LMI) approach to design a continuous gain scheduled H∞ controller, which is applicable to systems with fast state variations so that the deficiency of the coventional gain scheduled controllers is overcome, with the use of Taylor series fitting. Fuzzy control is then incorporated so that the design controller possesses the characteristics of a fast response for large errors and a well-damped response for small errors. The system thus always has a good dynamic performance along with the variations of the system states. Simulations and experiments demonstrate the effectiveness of the designed controller.

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

2005 ◽  
Vol 10 (4) ◽  
pp. 333-342
Author(s):  
V. Chadyšas ◽  
D. Krapavickaitė
Keyword(s):  
Mean Square Error ◽  
Taylor Series ◽  
Random Sampling ◽  
Finite Population ◽  
Taylor Series Expansion ◽  
Simple Random Sampling ◽  
Population Parameter ◽  
Mean Square ◽  
Parameter Ratio ◽  
Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

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