Robust Kalman filtering for nonlinear multivariable stochastic systems in the presence of non‐Gaussian noise

The presence of outliers can considerably degrade the performance of linear recursive algorithms based on the assumptions that measurements have a Gaussian distribution. Namely, in measurements there are rare, inconsistent observations with the largest part of population of observations (outliers). Therefore, synthesis of robust algorithms is of primary interest. The Masreliez–Martin filter is used as a natural frame for realization of the state estimation algorithm of linear systems. Improvement of performances and practical values of the Masreliez‐Martin filter as well as the tendency to expand its application to nonlinear systems represent motives to design the modified extended Masreliez–Martin filter. The behaviour of the new approach to nonlinear filtering, in the case when measurements have non‐Gaussian distributions, is illustrated by intensive simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

Distributed Subgradient Methods for Multi-Agent Optimization

We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For solving this (not necessarily smooth) optimization problem, we consider a subgradient method that is distributed among the agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other agents in the network over a time-varying topology. We provide convergence results and convergence rate estimates for the subgradient method. Our convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.     

Luminescence properties of oxide films formed by anodization of aluminum in 12-tungstophosphoric acid

In this paper, we have investigated luminescence properties of oxide films formed by anodization of aluminum in 12-tungstophosphoric acid. For the first time we have measured weak luminescence during anodization of aluminum in this electrolyte (so-called galvanoluminescence GL) and showed that there are wide GL bands in the visible region of the spectrum and observed two dominant spectral peaks. The first one is at about 425 nm, and the second one shifts with anodization voltage. As the anodization voltage approaches the breakdown voltage, a large number of sparks appear superimposed on the anodic GL. Several intensive band peaks were observed under breakdown caused by electron transitions in W, P, Al, O, H atoms. Furthermore, photoluminescence (PL) of anodic oxide films and anodic-spark formed oxide coatings were performed. In both cases wide PL bands in the range from 320 nm to 600 nm were observed.     

Value of security: modeling time-dependent phenomena and weather conditions

Deterministic security criteria provide a degree of security that may be insufficient under some operating conditions and excessive for others. To determine an appropriate level of security, one should perform a probabilistic cost/benefit analysis that balances the cost of the security margin against its benefits, i.e., the expected societal cost of the avoided outages. This paper shows how a previously published method based on Monte Carlo simulation can be enhanced to take into account time-dependent phenomena (TDP) such as cascade tripping of elements due to overloads, malfunction of the protection system, and potential power system instabilities. In addition, the importance of using failure rates that reflect the weather conditions is discussed. Studies based on the South-Western part of the transmission network of England and Wales demonstrate the validity of the models that have been developed.     

Application of the optimal power flow model in power systemeducation

Application of the optimal power flow based software package to aid instruction in the classroom is given in this paper. The package is used in the courses Computer Methods in Power Engineering, and Planning, Operation and Control of Power Systems. The essential idea is to utilize all calculation modules from the previously developed optimal power flow model. The software is applied in four projects, that is, constrained optimization, transmission expansion planning, active and reactive dispatching for operations planning, and corrective rescheduling within real time control     

Distributed and Recursive Parameter Estimation in Parametrized Linear State-Space Models

We consider a network of sensors deployed to sense a spatio-temporal field and infer parameters of interest about the field. We are interested in the case where each sensor's observation sequence is modeled as a state-space process that is perturbed by random noise, and the models across sensors are parametrized by the same parameter vector. The sensors collaborate to estimate this parameter from their measurements, and to this end we propose a distributed and recursive estimation algorithm, which we refer to as the incremental recursive prediction error algorithm. This algorithm has the distributed property of incremental gradient algorithms and the on-line property of recursive prediction error algorithms.      

Evaluation of periosteal membranes and coronally positioned flaps in the treatment of Class II furcation defects: a comparative clinical study in humans

The purpose of this study was to compare the clinical effectiveness of connective tissue grafts including periosteum used as a mechanical barrier for guided periodontal tissue regeneration and coronally positioned flaps in the treatment of Class II furcation defects. A total of 28 furcation defects were treated; 14 received a periosteal barrier and 14 received a coronally positioned flap. Reentry surgeries were performed at 6 months. No statistically significant differences were found preoperatively between the two treatment groups with respect to clinical parameters and osseous measurements. Postsurgically, both treatment modalities resulted in a significant decrease in probing depth and a significant gain in clinical attachment, but the differences observed were not statistically significant. The periosteal barrier group presented with a significantly better gain in vertical components of the alveolar bone (1.93 +/- 0.15 mm and 0.20 +/- 0.26 mm for periosteal barrier and coronally positioned flap groups, respectively; P < or = 0.001) and horizontal components of the alveolar bone (1.60 +/- 0.21 mm and 0.13 +/- 0.90 mm for periosteal barrier and coronally positioned flap groups, respectively; P < or = 0.001). The results of this trial indicate that similar clinical resolution of Class II furcation defects can be obtained with periosteal barriers and coronally positioned flaps. Periosteal barriers, however, are a better treatment alternative in achieving bone fill of the furcation area.     

Optimal cascade hydraulic control for a parallel robot platform by PSO

A new cascade load force control design for a parallel robot platform is proposed. A parameter search for a proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. A parameter search based on particle swarm optimization (PSO) is suggested to effectively search the parameters of the cascade controller. We used a unified mathematical model of a hydraulic actuator of parallel robot platform. These equations are readily applicable to various types of proportional valves, and they unify the cases of critical center, overlapped, and underlapped valves. These unified model equations are useful for nonlinear controller design. Simulation results show the advantages of the proposed optimal tuned cascade controller to solve the formulated tracking problem in relation to the classical proportional–integral (PI) controller.     

Identification of time‐varying OE models in presence of non‐Gaussian noise: Application to pneumatic servo drives

Intensive research in the field of mathematical modeling of pneumatic servo drives has shown that their mathematical models are nonlinear in which many important details cannot be included in the model. Owing the influence of the combination of heat coefficient, unknown discharge coefficient, and change of temperature, it was supposed that parameters of the pneumatic cylinder are random (stochastic parameters). On the other side, it has been well known that the nonlinear model can be approximated by a linear model with time‐varying parameters. Due to the aforementioned reasons, it can be assumed that the pneumatic cylinder model is a linear stochastic model with variable parameters. In practical conditions, in measurements, there are rare, inconsistent observations with the largest part of population of observations (outliers). Therefore, synthesis of robust algorithms is of primary interest. In this paper, the robust recursive algorithm for output error models with time‐varying parameters is proposed. The convergence property of the proposed robust algorithm is analyzed using the methodology of an associated ordinary differential equation system. Because ad hoc selection of model orders leads to overparameterization or parsimony problem, the robust Akaike's criterion is proposed to overcome these problems. By determining the least favorable probability density for a given class of probability distribution represents a base for design of the robust version of Akaike's criterion. The behavior of the proposed robust identification algorithm is considered through intensive simulations that demonstrate the superiority of the robust algorithm in relation to the linear algorithms (derived under an assumption that the stochastic disturbance has a Gaussian distribution). The good practical values of the proposed robust algorithm to identification of the pneumatic cylinder are illustrated by experimental results. Copyright © 2016 John Wiley & Sons, Ltd.