tthe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. this paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. as a novelty, the proposed controller employs a simple gaussian radial-basis-function network as an uncertainty estimator. the proposed netw...

The paper discusses the location-allocation model for logistic networks and distribution centers through considering uncertain parameters. In real-world cases, demands and transshipment costs change over the period of the time. This may lead to large cost deviation in total cost. Scenario based robust optimization approaches are proposed where occurrence probability of each scenario is not know...

The purpose of this study is to investigate the accuracy of predictions of aniline removal efficiency in a moving bed biofilm reactor (MBBR) by various methods, namely by RBF, ANFIS, and fuzzy regression analysis. The reactor was operated in an aerobic batch and was filled by light expanded clay aggregate (LECA) as a carrier for the treatment of Aniline synthetic wastewater. Exploratory data an...

The field of image set compression, which refers to the compression of image sets like photo albums and medical images, is becoming increasingly relevant in today’s digital world. In recent years, several schemes for image set compression have been proposed. In this paper, we propose a new SURF based image set compression scheme which uses SURF and HEVC. We also provide comparison results of ou...

In this paper, a robust autopilot is proposed using stable interpolation based on Youla parameterization. The most important condition of stable interpolation between local controllers is the preservation of stability so that each local controller can ensure stability for an open neighborhood around a nominal point. The proposed design used fixed-order robust controller with parameter-dependent...

We consider optimization problems whose parameters are known only approximately, based on noisy samples. Of particular interest is the high-dimensional regime, where the number of samples is roughly equal to the dimensionality of the problem, and the noise magnitude may greatly exceed the magnitude of the signal itself. This setup falls far outside the traditional scope of Robust and Stochastic...