Uncertainty Theory Based Partitioning for Cyber-Physical Systems with Uncertain Reliability Analysis

Reasonable partitioning is a critical issue for cyber-physical system (CPS) design. Traditional CPS partitioning methods run in a determined context and depend on the parameter pre-estimations, but they ignore the uncertainty of parameters and hardly consider reliability. The state-of-the-art work proposed an uncertainty theory based CPS partitioning method, which includes parameter uncertainty and reliability analysis, but it only considers linear uncertainty distributions for variables and ignores the uncertainty of reliability. In this paper, we propose an uncertainty theory based CPS partitioning method with uncertain reliability analysis. We convert the uncertain objective and constraint into determined forms; such conversion methods can be applied to all forms of uncertain variables, not just for linear. By applying uncertain reliability analysis in the uncertainty model, we for the first time include the uncertainty of reliability into the CPS partitioning, where the reliability enhancement algorithm is proposed. We study the performance of the reliability obtained through uncertain reliability analysis, and experimental results show that the system reliability with uncertainty does not change significantly with the growth of task module numbers.

Issues of Designing a Model Adaptive Controller Without a State Observer

It can be challenging to develop a controller using conventional techniques for a plant with a linear or nonlinear dynamical system or model uncertainty. Model adaptive control is a new alternative to classical control techniques and a simple way to update controller parameters. Because model reference adaptive control is unable to anticipate the state in real time if the state observer is not designed with, we will review some of the most major disadvantages of the most commonly used design techniques without state observer in this work.

Modelling evaporation with local, regional and global BROOK90 frameworks: importance of parameterization and forcing

Observation and estimation of evaporation is a challenging task. Evaporation occurs on each surface and is driven by different energy sources. Thus the correct process approximation in modelling of the terrestrial water balance plays a crucial part. Here, we use a physically-based 1D lumped soil-plant-atmosphere model (BROOK90) to study the role of parameter selection and meteorological input for modelled evaporation on the point scale. Then, with the integration of the model into global, regional and local frameworks, we made cross-combinations out of their parameterization and forcing schemes to analyse the associated model uncertainty. Five sites with different land uses (grassland, cropland, deciduous broadleaf forest, two evergreen needleleaf forests) located in Saxony, Germany were selected for the study. All combinations of the model setups were validated using FLUXNET data and various goodness of fit criteria. The output from a calibrated model with in-situ meteorological measurements served as a benchmark. We focused on the analysis of the model performance with regard to different time-scales (daily, monthly, and annual). Additionally, components of evaporation are addressed, including their representation in BROOK90. Finally, all results are discussed in the context of different sources of uncertainty: model process representation, input meteorological data and evaporation measurements themselves.

Introduction to the Theory of Imprecise Probability

The theory of imprecise probability is a generalization of classical ‘precise’ probability theory that allows modeling imprecision and indecision. This is a practical advantage in situations where a unique precise uncertainty model cannot be justified. This arises, for example, when there is a relatively small amount of data available to learn the uncertainty model or when the model’s structure cannot be defined uniquely. The tools the theory provides make it possible to draw conclusions and make decisions that correctly reflect the limited information or knowledge available for the uncertainty modeling task. This extra expressivity however often implies a higher computational burden. The goal of this chapter is to primarily give you the necessary knowledge to be able to read literature that makes use of the theory of imprecise probability. A secondary goal is to provide the insight needed to use imprecise probabilities in your own research. To achieve the goals, we present the essential concepts and techniques from the theory, as well as give a less in-depth overview of the various specific uncertainty models used. Throughout, examples are used to make things concrete. We build on the assumed basic knowledge of classical probability theory.

Robustness Analysis of an Electrohydraulic Steering Control System Based on the Estimated Uncertainty Model

The impossibility of replacing hydraulic drives with other type drives in heavy duty machinery is the main reason for the development of a system for controlling hydraulic power steering. Moreover, the need for remote automatic control of the steering in specific types of mobile machinery leads to significant scientific interest in the design of embedded systems for controlling electro-hydraulic steering units. This article introduces an approach, which has been developed by authors, for robust stability and robust performance analysis of two embedded systems for controlling electro-hydraulic power steering in mobile machinery. It is based on the suggested new more realistic “black box” SIMO model with output multiplicative uncertainty. The uncertainty is obtained by parameters confidence interval and Gauss approximation formula. The embedded control systems used a linear-quadratic Gaussian (LQG) controller and H∞ controller. The synthesis of the controllers was performed on the basis of a nominal part of an uncertainty model. Robust stability and robust performance analyses were performed in the framework of a so-called structured singular value, μ. The obtained theoretical results were experimentally approved by real experiments with both of the developed control systems, which were physically realized as a laboratory test rig.

Subsurface-to-Economics Closing the Loop on Uncertainty Analysis

We are all aware that our future is uncertain. Although some aspects can be predicted with more certainty and others with less, essentially everything is uncertain. Uncertainty exists because of lack of data, lack of resources, and lack of understanding. We cannot measure everything, so there are always unknowns. Even measurements include measurement errors. Also, we do not always have enough resources to analyze the data obtained. In addition, we do not have a full understanding of how the world, or the universe, works (Park 2011). Every day we find ourselves in situations where we must make many decisions, big or small. We tend to make the decisions based on a prediction, despite knowing that it is uncertain. For instance, imagine how many decisions are made by people every day based on the probability of it raining tomorrow (i.e., based on the weather forecast). To have a good basis for making a decision, it is of critical importance to correctly model the uncertainty in the forecast. In the oil and gas industry, uncertainties are large and complex. Oil and gas fields have been developed and operated despite tremendous uncertainty in a variety of areas, including undiscovered media and unpredictable fluid in the subsurface, wells, unexpected facility and equipment costs, and economic, political, international, environmental, and many other risks. Another important aspect of uncertainty modeling is the feasibility of verifying the uncertainty model with the actual results. For example, in the weather forecast it was announced that the probability of raining the next day was 20%. And the next day it rained. Do we say the forecast was wrong? Can we say the forecast was right? In order to make sure the uncertainty model is correct; we should strictly verify all the assumptions and follow the mathematically, statistically, proven-to-be-correct methodology to model the uncertainty (Caers et al. 2010; Caers 2011). In this paper, we show an effective, rigorous method of modeling uncertainty in the expected performance of potential field development scenarios in the oil and gas field development planning given uncertainties in various domains from subsurface to economics. The application of this method is enabled by using technology as described in a later section.

Data-Driven Robust Resource Allocation with Monotonic Cost Functions

Using Data to Allocate Resources Efficiently In city logistics systems, a fleet of vehicles is divided between service regions that function autonomously. Each region finds optimal routes for its own fleet and incurs costs accordingly. More vehicles lead to lower costs, but the trade-off is that fewer vehicles are left for other regions. Costs are difficult to quantify precisely because of demand uncertainty but can be estimated using data. The paper “Data-driven robust resource allocation with monotonic cost functions” by Chen, Marković, Ryzhov, and Schonfeld develops a principled risk-averse approach for two-stage resource allocation. The authors propose a new uncertainty model for decreasing cost functions and show how it can be leveraged to efficiently find resource allocations that demonstrably reduce the frequency of high-cost scenarios. This framework combines statistics and optimization in a novel way and is applicable to a general class of resource allocation problems, encompassing facility location, vehicle routing, and discrete-event simulation.