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We propose a swarm control algorithm for unmanned vehicles that adapts to unexpected environments while making the optimal formation. Our proposed algorithm, “autonomous and adaptive control”, is inspired by the control mechanism of living organisms and reconciles adaptability under a complex and changing environment and optimality for the various purposes of the system. In this paper, we apply the...
Modeling the spatial variation of resources is necessary because it gives an estimate of what to expect during their exploration and exploitation. We focus on the spatial modeling of polymetallic nodules found in the deep sea regions of the Clarion-Clipperton zone in the Pacific. The data from this region available in the open domain is sparse, which warrants modeling techniques that can efficiently...
We investigate methods to define a probabilistic logic and their application to multi-source fusion problems in geospatial decision support systems1. We begin with a discussion of augmenting propositional calculus with probabilities. Given a set of sentences, S, each with a known probability, the problem is to determine the probability of a query sentence that is a disjunction of literals appearing...
Article deals with the problem of simulation modeling of robust control system for nonlinear plants with the input signal saturation, functioning under a priori uncertainty conditions and in the presence of several statement delays. With the help of computational experiments, the quality of the control system operation under various initial conditions of the controlled plant is illustrated.
A major limitation of mobile Crowd Sourcing (CS) applications is the generation of false (or spam) contributions due to selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue through disbursement of undue incentives and also negatively affects the application's operational reliability. In this work, we propose a reputation model,...
This paper presents a design methodology with uncertainty quantification to estimate the required margin for two main design variables in a modular multilevel converter (MMC). In this methodology, the minimum required design margins are calculated by quantifying all sources of uncertainty in the modeling and simulation of MMCs. To this end, an enhanced modeling framework is presented to take into...
The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic...
Robust state estimation of a discrete time-varying uncertain system is investigated in this paper when there are not only process and measurement noise, but also parameter uncertainties which affect a state-space model arbitrarily. The expectation minimization based robust state estimation is generalized to uncertain linear systems with a known deterministic input signal. The derived robust estimator...
In this paper, we propose a control model based on NCS theory to solve the vehicle-following problem, which presents both issues: the difficulty in the identification process and a lossy network. To deal with network losses, we use a mode dependent Markov jump linear filter coupled with a statefeedback controller. To handle parameter uncertainty, a polytopic representation of the car model is presented...
At Ka-band frequencies, small deviations in the antenna production process result in sub-optimal performance. Classically, Monte-Carlo methods are used to quantify the influence of those tolerances. In this application, Monte-Carlo simulations are not feasible due to the high computational effort and the high number of parameters to be considered. As an alternative, we use polynomial chaos expansion...
Continuous correction of train absolute position in local area is accomplished by connecting distance detect sensor group to on-board Automatic Train Protection (ATP) system and installing the height difference positioning matrix on the wayside. The on-board ATP system can detect every unit height of the height difference positioning matrix row element, and transfer the heights into a unique position...
We address a novel probabilistic approach to estimate the Worst Case Response Time boundaries of tasks. Multi-core real-time systems process tasks in parallel on two or more cores. Tasks in our contribution may preempt other tasks, block tasks with semaphores to access global shared resources, or migrate to another core. The depicted task behavior is random. The shape of collected response times of...
The solution of the problem of construction of generalized model of production system optimal development is considered. Basis of the model — Bolza variational problem with integrated criterion of the first kind. Solution of the problem by numerical method is based on the method of optimal aggregation of “production, development” structures by simulation method of Hamilton function determination and...
The article considers methods of processing uncertainties in solving dynamic planning problems. Various types of uncertainties are considered, such as stochastic uncertainties, uncertainties in the parameters and structure of models, the uncertainty of the amplitude type and the probabilistic type. Methods for processing data for reducing uncertainties are proposed.
The collaborative recommendation mechanism is beneficial for the subject in an open network to find efficiently enough referrers who directly interacted with the object and obtain their trust data. The uncertainty analysis to the collected trust data selects the reliable trust data of trustworthy referrers, and then calculates the statistical trust value on certain reliability for any object. After...
This paper presents a metamodel based on the sparse polynomial chaos approach, well adapted to high-dimensional uncertainty quantification problems, applied for the analysis of crosstalk in printed circuit board microstrip traces. It enables to estimate, with a low computational cost compared to Monte Carlo (MC) simulation, statistical quantities and provides a sensitivity analysis of the crosstalk...
Large-scale inverse problems and uncertainty quantification (UQ), i.e., quantifying uncertainties in complex mathematical models and their large-scale computational implementations, is one of the outstanding challenges in computational science and will be a driver for the acquisition of future supercomputers. These methods generate significant amounts of simulation data that is used by other parts...
In this paper, the problem of flocking of MultiAgent Systems (MAS) in presence of system uncertainties and unknown disturbances is investigated. A biologically-inspired novel distributed resilient controller based on a computational model of emotional learning in mammalian brain is proposed. The methodology, known as Brain Emotional Learning Based Intelligent Controller (BELBIC), embeds a resilience...
To apply the smart wolf pack algorithm to solve the uncertain bilevel knapsack problem effectively, a binary smart wolf pack algorithm is designed. Firstly, the paper proposes an uncertain bilevel knapsack problem model by introducing uncertainty theory to the traditional bilevel knapsack problem model. Secondly, to solve the model of uncertain bilevel knapsack problem by the algorithm directly, we...
In this paper, we develop a data-driven model of a model-sized aircraft using system identification (SysID) techniques. The emphasis is placed on multiple short data records that are used in obtaining an initial model of the system. The records are “short” with respect to the length of a “typical” identification experiment and are necessary because of the unstable nature of the open-loop system. Owing...
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