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...

This paper proposed a novel adaptive robust backstepping control scheme for DC-DC buck converter subjected to external disturbance and system uncertainty. Uncertainty in the load resistance input voltage represent big challenge control. In this work, an estimator matched mismatched uncertainties based is applied converter. The updating laws are determined on lyapunov theorem. Thus, difference b...

To facilitate offline reinforcement learning, uncertainty estimation is commonly used to detect out-of-distribution data. By inspecting, we show that current explicit estimators such as Monte Carlo Dropout and model ensemble are not competent provide trustworthy in learning. Accordingly, propose a non-parametric distance-aware estimator which sensitive the change input space for Based on our ne...

`Abstract: A new robust estimator based on the concept of uncertainty in the measurements is developed in this paper. The uncertainty in the measurements is modeled via deterministic upper and lower bounds on measurement errors, which take into account known meter accuracies. Inequality constraints are constructed to model the uncertainty in the measurements. A solution point satisfying most in...

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...

The goal of this paper is to introduce a partially adaptive estimator for the censored regression model based on an error structure described by a mixture of two normal distributions. The model we introduce is easily estimated by maximum likelihood using the EM algorithm adapted from the work of Bartolucci and Scaccia (2004). A Monte Carlo study is conducted to examine the small sample properti...

In this paper, a novel stator current based Model Reference Adaptive System (MRAS) estimator for speed estimation in the speed-sensorless vector controlled induction motor drives is presented. In the proposed MRAS estimator, measured stator current of the induction motor is considered as a reference model. The estimated stator current is produced in an adjustable model to compare with the measu...

Di Crescenzo and Longobardi (2002) has been proposed a measure of uncertainty related to past life namely past entropy. The present paper addresses the question of extending this concept to bivariate set-up and study some properties of the proposed measure. It is shown that the proposed measure uniquely determines the distribution function. Characterizations for some bivariate lifetime models a...