Hierarchical Control Co-Design Using a Model Fidelity-Based Decomposition Framework

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Austin L. Nash ◽  
Neera Jain
Keyword(s):  
Hierarchical Control ◽  
Coupled Systems ◽  
System Level ◽  
Design Stage ◽  
System Component ◽  
Model Parameters ◽  
Level Control ◽  
Efficient Manner ◽  
Model Fidelity ◽  
Time Operation

Abstract Increasing performance demands and constraints are necessitating the design of highly complex, integrated systems across multiple sectors, including transportation and energy. However, conventional design approaches for such systems are largely siloed and focused on steady-state operation. To accommodate tightening operating envelopes, new design paradigms are needed that explicitly consider system-component interactions and their implications on transient performance at the system design stage. In this work, we present a model fidelity-based decomposition (MFBD) hierarchical control co-design (HCCD) algorithm designed to optimize system performance characteristics, with an emphasis on robustness to transient disturbances during real-time operation. Our framework integrates system level control co-design (CCD) with high-fidelity component design optimization in a computationally efficient manner for classes of highly coupled systems in which the coupling between subproblems cannot be fully captured using existing analytical relationships. Our algorithm permits scalable decomposition of computationally intensive component models and addresses coupling issues between subproblems in part by introducing an intermediate optimization procedure to solve for reduced-order model parameters that maximize the accuracy of the lumped-parameter control model required in the CCD algorithm. We demonstrate the merits of the MFBD HCCD algorithm, in comparison to an all-at-once (AAO) CCD approach, through a case study on aircraft dynamic thermal management. Our results show that our decomposition-based solution matches the AAO optimal cost to within 2.5% with a 54% reduction in computation time.

UNCERTAINTY ASSESSMENT TECHNIQUES FOR SYSTEM AVAILABILITY

2009 ◽  
Vol 16 (01) ◽  
pp. 39-57
Author(s):  
MEHMET MIMAN ◽  
EDWARD A. POHL
Keyword(s):  
Cost Model ◽  
Uncertainty Assessment ◽  
Assessment Tools ◽  
System Level ◽  
Design Stage ◽  
Importance Measure ◽  
Efficient Manner ◽  
System Availability ◽  
Common System ◽  
Assessment Techniques

This paper provides an approach for assessing the uncertainty associated with the estimate of the availability of a two-state repairable system. During the design stage it is often necessary to allocate scarce testing resources among various components in an efficient manner. Although there are a variety of importance and uncertainty measures for the reliability of a system, there are limited measures for systems availability. This study attempts to fill the gaps on availability importance measures and provide insights for techniques to reduce the variance of a system-level availability estimate efficiently. The variance importance measure is constructed such that it provides a measure of the improvement in the variance of the system level availability estimate through the reduction of the variance of the various component availability estimates. In addition, a cost model is developed that trades-off cost and uncertainty. The measure is illustrated for five common system structures. Monte Carlo Simulation is used to illustrate the use of the assessment tools on a specific problem. Observations conclude that results are consistent with reliability importance measures.

Dynamic Infrared System Level Fault Isolation

2003 ◽  
Author(s):  
John A. Naoum ◽  
Johan Rahardjo ◽  
Yitages Taffese ◽  
Marie Chagny ◽  
Jeff Birdsley ◽  
...  
Keyword(s):  
Fault Isolation ◽  
System Level ◽  
System Component ◽  
Component Level ◽  
Ir Imaging ◽  
Non Destructive

Abstract The use of Dynamic Infrared (IR) Imaging is presented as a novel, valuable and non-destructive approach for the analysis and isolation of failures at a system/component level.

scholarly journals A Multi Objective Evolutionary Algorithm for the Parameters Extraction of Organic Thin Film Transistors Models

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 939
Author(s):  
Rosario Schiano Lo Moriello ◽  
Davide Ruggiero ◽  
Leopoldo Angrisani ◽  
Enzo Caputo ◽  
Francesco de Pandi ◽  
...  
Keyword(s):  
Thin Film ◽  
Evolutionary Algorithm ◽  
Design Stage ◽  
Model Parameters ◽  
Organic Transistors ◽  
Organic Thin Film ◽  
Multi Objective ◽  
Application Fields ◽  
Channel Lengths ◽  
P Type

Thanks to their peculiar features, organic transistors are proving to be a valuable alternative to traditional semiconducting devices in several application fields; however, before releasing their exploitation, simulating their behaviour through adequate circuital models could be advisable during the design stage of electronic circuits and/or boards. Consequently, accurately extracting the parameter value of those models is fundamental to developing useful libraries for hardware design environments. To face the considered problem, the authors present a method based on successive application of Single- and Multi-Objective Evolutionary Algorithm for the optimal tuning of model parameters of organic transistors on thin film (OTFT). In particular, parameters are first roughly estimated to assure the best fit with the experimental transfer characteristics; the estimates are successively refined through the multi-objective strategy by also matching the values of the experimental mobility. The performance of the method has been assessed by estimating the parameters value of both P-type and N-type OTFTs characterized by different values of channel lengths; the obtained results evidence that the proposed method can obtain suitable parameters values for all the considered channel lengths.

scholarly journals System Level Simulation of Microgrid Power Electronic Systems

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 644
Author(s):  
Michal Frivaldsky ◽  
Jan Morgos ◽  
Michal Prazenica ◽  
Kristian Takacs
Keyword(s):  
Smart Grid ◽  
Simulation Model ◽  
Real Time ◽  
Power Converter ◽  
System Level ◽  
Time Behavior ◽  
Operational Parameters ◽  
System Level Simulation ◽  
Time Operation ◽  
The Impact

In this paper, we describe a procedure for designing an accurate simulation model using a price-wised linear approach referred to as the power semiconductor converters of a DC microgrid concept. Initially, the selection of topologies of individual power stage blocs are identified. Due to the requirements for verifying the accuracy of the simulation model, physical samples of power converters are realized with a power ratio of 1:10. The focus was on optimization of operational parameters such as real-time behavior (variable waveforms within a time domain), efficiency, and the voltage/current ripples. The approach was compared to real-time operation and efficiency performance was evaluated showing the accuracy and suitability of the presented approach. The results show the potential for developing complex smart grid simulation models, with a high level of accuracy, and thus the possibility to investigate various operational scenarios and the impact of power converter characteristics on the performance of a smart gird. Two possible operational scenarios of the proposed smart grid concept are evaluated and demonstrate that an accurate hardware-in-the-loop (HIL) system can be designed.

Neural network modeling of the efficiency of response to emergency situations in a multi-level control system

2021 ◽  
Vol 92 ◽  
pp. 79-93
Author(s):  
N. G. Topolsky ◽  
◽  
S. Y. Butuzov ◽  
V. Y. Vilisov ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Systems ◽  
Hierarchical Control ◽  
Hierarchical Systems ◽  
Level Control ◽  
Emergency Situations ◽  
Expert Assessments ◽  
Multi Level ◽  
The Relationship

Introduction. It is important to have models that adequately describe the relationship between the integral indicators of the functioning of the system with the particular indicators of the lower levels of management in complex control systems, in particular in RSChS. Traditional approaches based on normative models often turn out to be untenable due to the impossibility of covering all aspects of the functioning of such systems, as well as due to the high variability of the environment and the values of the set of target indicators. Recently, adaptive machine-learning models have proven to be productive, allowing build stable and adequate models, one of the variants of which is artificial neural networks (ANN), based on the solution of inverse problems using expert estimates. The relevance of the study lies in the development of compact models that allow assessing the effectiveness of the functioning of complex multi-level control systems (RSChS) in emergency situations, developing according to complex scenarios, in which emergencies of various types can occur simultaneously. Goals and objectives. The purpose of the article is to build and test the technology for creating compact models that are adequate to the system of indicators of the functioning of hierarchically organized control systems. This goal gives rise to the task of choosing tools for constructing the necessary models and sources of initial data. Methods. The research tools include methods for analyzing hierarchical systems, mathematical statistics, machine learning methods of ANN, simulation modeling, expert assessment methods, software systems for processing statistical data. The research is based on materials from domestic and foreign publications. Results and discussion. The proposed technology for constructing a neural network model of the effectiveness of the functioning of complex hierarchical systems provides a basis for constructing dynamic models of this type, which make it possible to distribute limited financial and other resources during the operation of the system according to a complex scenario of emergency response. Conclusion. The paper presents the results of solving the problem of constructing an ANN and its corresponding nonlinear function, reflecting the relationship between the performance indicators of the lower levels of the hierarchical control system (RSChS) with the upper level. The neural network model constructed in this way can be used in the decision support system for resource management in the context of complex scenarios for the development of emergency situations. The use of expert assessments as an information basis makes it possible to take into account numerous target indicators, which are extremely difficult to take into account in other ways. Keywords: emergency situations, hierarchical control system, efficiency, artificial neural network, expert assessments

Identification of Tire Model Parameters Through Full Vehicle Experimental Tests

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
Francesco Braghin ◽  
Federico Cheli ◽  
Edoardo Sabbioni
Keyword(s):  
Laboratory Tests ◽  
Experimental Tests ◽  
Contact Forces ◽  
Lateral Acceleration ◽  
Design Stage ◽  
Model Parameters ◽  
Tire Model ◽  
Identification Procedure ◽  
Sideslip Angle ◽  
Tyre Model

Individual tire model parameters are traditionally derived from expensive component indoor laboratory tests as a result of an identification procedure minimizing the error with respect to force and slip measurements. These parameters are then transferred to vehicle models used at a design stage to simulate the vehicle handling behavior. A methodology aimed at identifying the Magic Formula-Tyre (MF-Tyre) model coefficients of each individual tire for pure cornering conditions based only on the measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed and steer angle) during standard handling maneuvers (step-steers) is instead presented in this paper. The resulting tire model thus includes vertical load dependency and implicitly compensates for suspension geometry and compliance (i.e., scaling factors are included into the identified MF coefficients). The global number of tests (indoor and outdoor) needed for characterizing a tire for handling simulation purposes can thus be reduced. The proposed methodology is made in three subsequent steps. During the first phase, the average MF coefficients of the tires of an axle and the relaxation lengths are identified through an extended Kalman filter. Then the vertical loads and the slip angles at each tire are estimated. The results of these two steps are used as inputs to the last phase, where, the MF-Tyre model coefficients for each individual tire are identified through a constrained minimization approach. Results of the identification procedure have been compared with experimental data collected on a sport vehicle equipped with different tires for the front and the rear axles and instrumented with dynamometric hubs for tire contact forces measurement. Thus, a direct matching between the measured and the estimated contact forces could be performed, showing a successful tire model identification. As a further verification of the obtained results, the identified tire model has also been compared with laboratory tests on the same tire. A good agreement has been observed for the rear tire where suspension compliance is negligible, while front tire data are comparable only after including a suspension compliance compensation term into the identification procedure.

Identification of Human Errors During Early Design Stage Functional Failure Analysis

2018 ◽  
Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Failure Analysis ◽  
Human Error ◽  
System Level ◽  
Design Stage ◽  
Human Factors Engineering ◽  
Human Errors ◽  
Functional Failure ◽  
Early Design ◽  
Early Design Stage ◽  
Early Design Stages

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced FFIP (Functional Failure Identification and Propagation), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed towards the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. To explore the capabilities of the proposed method, it is applied to a hold-up tank example and the results are coupled with Digital Human Modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.

scholarly journals Constructing Accurate Equivalent Electrical Circuit Models of Lithium Iron Phosphate and Lead–Acid Battery Cells for Solar Home System Applications

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2305 ◽  
Author(s):  
Yunhe Yu ◽  
Nishant Narayan ◽  
Victor Vega-Garita ◽  
Jelena Popovic-Gerber ◽  
Zian Qin ◽  
...  
Keyword(s):  
Lithium Iron Phosphate ◽  
Electrical Circuit ◽  
Design Stage ◽  
Model Parameters ◽  
Equivalent Electrical Circuit ◽  
Cell Level ◽  
Electrical Equivalent Circuit ◽  
Battery Model ◽  
Vrla Battery ◽  
Lead Acid

The past few years have seen strong growth of solar-based off-grid energy solutions such as Solar Home Systems (SHS) as a means to ameliorate the grave problem of energy poverty. Battery storage is an essential component of SHS. An accurate battery model can play a vital role in SHS design. Knowing the dynamic behaviour of the battery is important for the battery sizing and estimating the battery behaviour for the chosen application at the system design stage. In this paper, an accurate cell level dynamic battery model based on the electrical equivalent circuit is constructed for two battery technologies: the valve regulated lead–acid (VRLA) battery and the LiFePO 4 (LFP) battery. Series of experiments were performed to obtain the relevant model parameters. This model is built for low C-rate applications (lower than 0.5 C-rate) as expected in SHS. The model considers the non-linear relation between the state of charge ( S O C ) and open circuit voltage ( V OC ) for both technologies. Additionally, the equivalent electrical circuit model for the VRLA battery was improved by including a 2nd order RC pair. The simulated model differs from the experimentally obtained result by less than 2%. This cell level battery model can be potentially scaled to battery pack level with flexible capacity, making the dynamic battery model a useful tool in SHS design.

Modeling the Propagation of Failures in Software Driven Hardware Systems to Enable Risk-Informed Design

2008 ◽  
Author(s):  
David C. Jensen ◽  
Irem Y. Tumer ◽  
Tolga Kurtoglu
Keyword(s):  
Complex System ◽  
Product Life Cycle ◽  
System Modeling ◽  
Software Component ◽  
Temperature Data ◽  
System Level ◽  
Design Stage ◽  
Redundancy Management ◽  
Hardware System ◽  
Failure Propagation

Software-driven hardware configurations account for the majority of modern complex systems. The often costly failures of such systems can be attributed to software specific, hardware specific, or software/hardware interaction failures. The understanding of the propagation of failures in a complex system is critical because, while a software component may not fail in terms of loss of function, a software operational state can cause an associated hardware failure. The least expensive phase of the product life cycle to address failures is during the design stage. This results in a need to evaluate how a combined software/hardware system behaves and how failures propagate from a design stage analysis framework. Historical approaches to modeling the reliability of these systems have analyzed the software and hardware components separately. As a result significant work has been done to model and analyze the reliability of either component individually. Research into interfacing failures between hardware and software has been largely on the software side in modeling the behavior of software operating on failed hardware. This paper proposes the use of high-level system modeling approaches to model failure propagation in combined software/hardware system. Specifically, this paper presents the use of the Function-Failure Identification and Propagation (FFIP) framework for system level analysis. This framework is applied to evaluate nonlinear failure propagation within the Reaction Control System Jet Selection of the NASA space shuttle, specifically, for the redundancy management system. The redundancy management software is a subset of the larger data processing software and is involved in jet selection, warning systems, and pilot control. The software component that monitors for leaks does so by evaluating temperature data from the fuel and oxidizer injectors and flags a jet as having a failure by leak if the temperature data is out of bounds for three or more cycles. The end goal is to identify the most likely and highest cost paths for fault propagation in a complex system as an effective way to enhance the reliability of a system. Through the defining of functional failure propagation modes and path evaluation, a complex system designer can evaluate the effectiveness of system monitors and comparing design configurations.

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