This paper proposes a discrete-time repetitive optimal control of electrically driven robotic manipulators using an uncertainty estimator. The proposed control method can be used for performing repetitive motion, which covers many industrial applications of robotic manipulators. This kind of control law is in the class of torque-based control in which the joint torques are generated by permanen...

Abstract. This paper is a continuation of the work in [11] and [2] on the problem of estimating by a linear estimator, N unobservable input vectors, undergoing the same linear transformation, from noise-corrupted observable output vectors. Whereas in the aforementioned papers, only the matrix representing the linear transformation was assumed uncertain, here we are concerned with the case in wh...

This paper presents a new approach for estimating the state of a linear dynamic system when two different types of uncertainties are present simultaneously. The first type of uncertainty is a stochastic process with given distribution. The second type of uncertainty is only known to be bounded, the exact underlying distribution is unknown. This includes inequality constraints between state vari...

To properly interpret the quality of a gamma-spectroscopy measurement, an uncertainty estimate must be made. The uncertainty in the efficiency calibration is the dominant component to the total propagated measurement uncertainty for many types of measurements. Any deviations between the as-calibrated geometry and the as-measured geometry contribute to the total uncertainty. A mathematical techn...

Cluster-Weighted Modeling, a mixture density estimator around local models, is presented as a framework for the analysis, prediction and characterization of non-linear time series. First architecture, model estimation and characterization formalisms are introduced. The characterization tools include estimator uncertainty, predictor uncertainty and the correlation dimension of the data set. In t...

Abstract— We study robust H∞ coherent-classical estimation for a class of physically realizable linear quantum systems with parameter uncertainties. Such a robust coherent-classical estimator, with or without coherent feedback, can yield better disturbance-to-error performance than the corresponding robust purely-classical estimator for an uncertain plant. Moreover, coherent feedback allows for...

In practical applications of item response theory (IRT), item parameters are usually estimated first from a calibration sample. After treating these estimates as fixed and known, ability parameters are then estimated. However, the statistical inferences based on the estimated abilities can be misleading if the uncertainty of the item parameter estimates is ignored. Instead, estimated item param...

We present a new method for simulating and predicting hydrologic variables with uncertainty assessment provide example applications to river flows. The is identified the acronym “Bluecat” based on use of deterministic model which subsequently converted stochastic formulation. latter provides an adjustment statistical basis prediction along its confidence limits. distinguishing features proposed...