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We investigate constrained optimal control problems for linear stochastic dynamical systems evolving in discrete time. We consider minimization of an expected value cost over a finite horizon. Hard constraints are introduced first, and then reformulated in terms of probabilistic constraints. It is shown that, for a suitable parametrization of the control policy, a wide class of the resulting optimization...
This paper illustrates a stochastic Model Predictive Control (MPC) algorithm to control a linear system subject to additive zero-mean noise, state and input constraints. The algorithm proposed is computationally efficient since it can be formulated as a SemiDefinite Programming (SDP) problem and can thus be solved by interior-point methods. We also show that, under the hypotesis of bounded noise,...
The feasibility of guaranteeing the accuracy at filtering with substantial prior uncertainty in a class of input signals is analyzed. The class y2 containing nonstationary stochastic signals with the only known prior information — upper bounds of some derivatives is considered. The procedure of estimating the extreme attainable error dispersion at filtering the input signals of the class y2 at the...
This paper presents an approach based on Stochastic Data Envelopment Analysis (SDEA) for evaluating of shaping factors for performance assessment of electricity distribution units. This approach is applied to assessment of Iranian distribution units from 2001 to 2011. There are usually incomplete and stochastic data or lack of data with respect to electricity distribution companies. So, in this paper...
The nonlinear beam with vertical combined excitations is proposed as an energy harvester. The system is modelled as a cantilever beam with included a tip mass and piezoelectric patches which transduce the bending strains induced by both, the harmonic and as an additive stochastic forces. The excitation affects in vertical directions by kinematic forcing into electrical charge. The main goal is to...
Boolean networks have emerged as an effective model of the dynamical behavior of regulatory circuits consisting of bi-stable components, e.g. genes that can be in an activated or suppressed transcriptional state. Such Boolean circuits are not deterministic, nor are they directly observable. We present a methodology for fault detection in stochastic Boolean dynamical systems observed though noisy continuous...
A number of intriguing decision scenarios, such as order picking, revolve around partitioning a collection of objects so as to optimize some application specific objective function. In its general form, this problem is referred to as the Object Partitioning Problem (OOP), known to be NP-hard. We here consider a variant of OPP, namely the Stochastic Online Equi-Partitioning Problem (SO-EPP). In SO-EPP,...
The paper deals with production performance evaluation of the EU selected regions in the period 2000 – 2011. We estimate a translog stochastic production frontier using the true fixed-effects methods. We detect statistically significant savings with respect to the technical progress in capital input and consumption with respect to the technical progress in labor input. We evaluate the performance...
Modeling and analysis of low frequency noise in circuit simulators with time-varying bias conditions is a long-standing open problem. In this paper, we offer a definite solution for this problem and present a model for low-frequency noise that captures the internal, stochastic dynamics of the individual noise sources via dedicated internal pseudo nodes that are coupled with the rest of the circuit...
There has been considerable theoretical and application of data envelopment analysis (DEA) directed to environmental efficiency measurement due to its capability of accounting for undesirable outputs. However, once statistical noise as well as environmental effects considered, these methods might fail to provide an appropriate estimates as the non-stochastic nature of deterministic DEA-based model...
This paper discusses the state estimation task in the Van der Vusse reaction, which is a classical benchmark in a number of control studies in chemistry. Recently, it was shown that the extended Kalman filter is not able to estimate accurately the concentrations in this reaction on the basis of temperature measurements, only. Here, we demonstrate that modern state estimation methods, such as the continuous-discrete...
In this paper, a robust optimal control problem is investigated for continuous-time linear stochastic systems with dynamic uncertainties. A non-model based stochastic robust optimal control design methodology is employed to iteratively update the control policy online by directly using the online information. A robust adaptive dynamic programming (RADP) algorithm is developed, together with rigorous...
This paper proposes a reduction method of stochastic disturbance that is focused on haptic information in the micro space. Presently in the industry, haptic information is attracting attention as the tertiary media following audio and visual information. Haptic information has characteristics of bidirectionality and scaling that extended human ability by using master and slave robots. Especially,...
Noise characteristics for the Microsoft Kinect sensor are presented. Horizontal (x) and vertical (y) stochastic noise are measured using a novel 3D checker board. Results show that the noise is affected mostly by the depth at which the object is sensed and by the radial distance from the center of the field of view. Measurement-based models for the noise in horizontal and vertical axes are presented...
In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays. Different from the augmented method for dealing with delayed systems, a linear unbiased minimum-variance filter design method is proposed without augmenting the state vector, which...
This paper presents a new methodology to craft navigation functions for nonlinear systems with stochastic uncertainty. The method relies on the transformation of the Hamilton-Jacobi-Bellman (HJB) equation into a linear partial differential equation. This approach allows for optimality criteria to be incorporated into the navigation function, and generalizes several existing results in navigation functions...
In general, the evaluation of complicated stochastic systems is difficult only from the viewpoint of conventional structural approach based on their physical internal mechanisms. In this case, we adopt very often the regression analysis model between input and output signals from the viewpoint of functional approach. In the previous paper, an extended regression analysis method was proposed by considering...
In this work, the risk-sensitive optimal control equations for polynomial stochastic systems of third degree with exponential criterion to be minimized and parameter of diffusion into the state equations has been applied to the Fitz Hugh-Nagumo (Bon Hoeffervan der Pol) model. This model represents an excitable system with driven noise, which could be associated with diverse processes, from the kinetic...
This contribution concerns the unscented Kalman filter (UKF) for higher index nonlinear differential-algebraic equation (DAE) systems. First, a short introduction to DAE systems is given. A solution concept for nonlinear DAE systems is discussed focusing on properties which are essential for the application of the UKF algorithm. The introduction of a stochastic noise in DAE systems and the contrast...
The aim of the paper is to study the possibility of advancing noised non-Gaussian processes recognition using the features based on higher-order statistics. Several new recognition features based on the higher-order statistics, the basis of advancing recognition results in the presence of noise, decision rules and recognition system frameworks we proposed in the paper. The efficiency test of the proposed...
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