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Noise or uncertainty appear in many optimization processes when there is not a single measure of optimality or fitness but a random variable representing it. These kind of problems have been known for a long time, but there has been no investigation of the statistical distribution those random variables follow, assuming in most cases that it is distributed normally and, thus, it can be modelled via...
This paper presents a novel estimation problem of Markovian jump linear systems (MJLSs) with generalized unknown disturbances (GUDs) in measurements. In these systems, there exist multiple uncertainties such as Markovian switching parameters, the GUD and system noises. Here, the multi-mode complexity in original system is transformed into the randomness of parameters in new system by geometric augmentation...
A fusion methodology for tracks represented by Gaussian mixtures is proposed for distributed maneuvering target tracking with unknown correlation information between the local agents. For this purpose, Chernoff fusion is applied to the Gaussian mixtures provided by the local interacting multiple-model (IMM) filters. Chernoff fusion of Gaussian mixtures is achieved using a recently proposed method...
The problem of selecting a template that matches a given candidate signal is applicable across a wide variety of domains. Using the correlation coefficient as the avenue for selecting the winning template is perhaps the most common technique. The challenge lies in selecting the winning template when there is no clear separation between the correlation coefficient values of the winning template and...
While observation sets for an individual object in orbit can be quite data-sparse, the sheer number of objects in orbit makes the tracking problem as a whole data-rich. As such it is infeasible for humans to process these measurements manually. While orbit determination procedures are largely automated the resulting solutions can be quite poor when the assumed dynamics are flawed due to mismodeled...
This work focuses on developing a data-efficient strategy for radio tomographic imaging with Bayesian compressive sensing. The task of our data-efficient strategy is to identify the informative yet non-redundant radio links in an adaptive fashion, which aims at reducing the fading uncertainties as well as the number of the received signal strength (RSS) measurements required. Our main contribution...
Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) based controllers for a class of hybrid systems, namely autonomous hybrid system (AHS), are proposed in this work. The stability in the performance of EKF and UKF based controllers were analyzed using experimental setup of hybrid three-tank system under variety of real-time uncertainties and operating conditions such as servo-regulatory...
The paper is concerned with state predictors that include a disturbance dynamics capable of estimating the disturbance to be rejected. The disturbance dynamics is driven by an unknown input signal, the uncertainty input, which is the output of a dynamic feedback driven by the model error (plant minus model output). As an extension of classical observers, the paper shows the advantage of designing...
A measurement-based statistical verification approach is developed for systems with partly unknown dynamics. These grey-box systems are subject to identification experiments which, new in this contribution, enable accepting or rejecting system properties expressed in a linear-time logic. We employ a Bayesian framework for the computation of a confidence level on the properties and for the design of...
Trilateration is an effective way to localize a sensor network based on relative distance measures, but the conditions that guarantee the existence of a solution are quite restrictive. If the network topology is a unit disk graph, however, the localization of the network can be achieved also when the standard trilateration fails, using a priori information about “not being connected”. Such an information...
To address the safety issues in human robot interactions (HRI), a safe set algorithm (SSA) was developed previously. However, during HRI, the uncertainty levels are changing in different phases of the interaction, which is not captured by SSA. A safe exploration algorithm (SEA) is proposed in this paper to address the uncertainty levels in the robot control. To estimate the uncertainty levels online,...
Signal estimation in MIMO communications typically suffers from performance degradations due to imperfect channel state information (CSI). Traditional robustification schemes rely on assumptions about the model uncertainty and may result in conservative performance. We introduce a rank-reduction approach that enhances the performance in training-based applications. A sequence of reduced-rank channel...
In this paper, we apply inverse optimal control approaches in order to recover the cost function that can explain given observations, for a class of constrained optimization problems. The inverse optimal control was recently solved in an approximately optimal framework, meaning that the interest is in finding the proper criteria suitable for the system for which the decisions are approximately optimal...
This paper considers the role of a demand aggregator that manages a large number of consumer loads, with the objective of participating in the frequency regulation market. The key feature to be exploited is load deferrability in time, which enables the aggregator to adapt the consumption profile and thus reduce its own consumption of regulation, and even be a provider of regulation services to others...
A stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems with arbitrary time-invariant probabilistic uncertainties and additive Gaussian process noise. Closed-loop stability of the SMPC approach is established by appropriate selection of the cost function. Polynomial chaos is used for uncertainty propagation through system dynamics. The performance of the...
Incorporation of user feedback in enterprise management products can greatly enhance our understanding of modern technology challenges and amplify the ability for those products to home in to user environments. In this paper we present an entropy-based confidence determination approach to process user feedback data (direct or indirect) to automatically rank and update the beliefs of any recommender...
In this paper, we investigate a robust spectrum sensing scheme using multiple antennas, which exploits the information of eigenvalues' characteristics of the sample covariance matrix. The difference between the maximum eigenvalue and the average of the remainder eigenvalues of sample covariance matrix is utilized as the test statistic. Besides, by exploiting Gaussian approximation for the test statistic,...
In this paper, three individual indices, as well as a new comprehensive index, are introduced to evaluate prediction intervals. Then, two practical methods, namely, Interval Extension Method and Optimal Scalar Method are proposed to build the prediction intervals based on an ensemble of Extreme Learning Machines. Case studies on hour-ahead load interval forecasting with respect to Chicago Metro Area...
This paper deals with the problem of guaranteed distributed estimation under an asynchronous communication scheme. The objective is to estimate the state of a perturbed linear plant using a set of agents which communicate using a common network. The agents have access to a limited set of system outputs affected by bounded noises, so collaboration among them is needed. Both measurement noises and disturbances...
We consider linear continuous-time systems with multiplicative noise and polytopic type parameter uncertainty and we address the problems of H∞ state-feedback control and filtering of these systems. These problems are solved by applying a vertex dependent Lyapunov function that considerably reduces the over-design associated with the classical “quadratic” design that is based on a single Lyapunov...
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