in this paper, an observer based fuzzy adaptive controller (fac) is designed fora class of large scale systems with non-canonical non-affine nonlinear subsystems. it isassumed that functions of the subsystems and the interactions among subsystems areunknown. by constructing a new class of state observer for each follower, the proposedconsensus control method solves the problem of unmeasured sta...

In this paper, we propose a robust unsupervised change detection algorithm for multilook polarimetric synthetic aperture radar (PolSAR) data. The Hotelling-Lawley trace (HLT) statistic is used as a test statistic to measure the similarity of two covariance matrices. The generalized Kittler and Illingworth (K&I) minimum-error thresholding algorithm is then applied on the test statistic image to ...

We develop an asymptotic likelihood ratio test for multivariate lognormal data with a point mass at zero in each dimension. The test generalizes Wilks' lambda and Hotelling T-test to the case of semicontinuous data. Simulations show that the resulting test statistic attains the nominal Type I error rate and has good power for reasonable alternatives. We conclude with an application to explorati...

This paper introduces a robust task-space control scheme for a robotic system with an adaptive observer. The proposed approach does not require the availability of the system states and an adaptive observer is developed to estimate the state variables. These estimated states are then used in the control scheme. First, the dynamic model of a robot is derived. Next, an observer-based robust contr...

This paper fully discusses how to design an observer based decentralized fuzzy adaptive controller for a class of large scale multivariable non-canonical nonlinear systems with unknown functions of subsystems’ states. On-line tuning mechanisms to adjust both the parameters of the direct adaptive controller and observer that guarantee the ultimately boundedness of both the tracking error and tha...

This paper presents a theoretical development that extends results on the distribution of the sample correlation coefficient and the Fisher transform (Fisher, 1921). Several well known correlational procedures use the Fisher transform or its square as the basis of hypothesis testing. These procedures assume that the variance of the Fisher transform can be approximated adequately as 1/( 3) n − ....