Effects of electromagnetic interference on controller-computer upsets and system stability

Kim, Hyangkyoung; White, Allan L; Shin, Kang G.

doi:10.1109/87.826806

Cited by 22 publications

(10 citation statements)

References 2 publications

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“…A restart of the component with subsequent state synchronization is a typical strategy to restore a correct state. An example for an external fault is Electromagnetic Interference (EMI) [12]. So-called borderline faults are the class of faults that cannot be judged to be external or internal with respect to the component boundary.…”

Section: The Component Fault Modelmentioning

confidence: 99%

A Maintenance-Oriented Fault Model for the DECOS Integrated Diagnostic Architecture

Peti

Obermaisser

Ademaj

et al.

19th IEEE International Parallel and Distributed Processing Symposium

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The increasing use of electronics in the automotive and avionic domain has lead to dramatic improvements with respect to functionality, safety, and cost. However, with this growth of electronics the likelihood of failures due to faults originating from electronic equipment also increases. In order to tackle prevalent diagnostic problems such as the reduction of the fault-not-found ratio, a maintenance-oriented fault model is needed that serves as the basis for the classification of experienced failures.In this paper we introduce such a maintenance-oriented fault model that establishes the conceptual foundation of the diagnostic services of the DECOS integrated architecture. The fault model takes the component-based nature of today's distributed embedded systems into account. According to this model each experienced failure is classified according to the field replaceable units of the system.

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Section: The Component Fault Modelmentioning

confidence: 99%

A Maintenance-Oriented Fault Model for the DECOS Integrated Diagnostic Architecture

Peti

Obermaisser

Ademaj

et al.

19th IEEE International Parallel and Distributed Processing Symposium

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show abstract

“…The control constants are amplifying factors that are set by the designer to give the desired control performance, e.g., making the physical value assume the reference value as fast as possible (after a change of the reference value) without exceeding the reference value (i.e., overshooting). Previous research [4,5] has investigated how the stability of the system is affected by data errors. However, a system may fail before it reaches instability.…”

Section: Assumptions and Restrictionsmentioning

confidence: 99%

“…3.1). Thus, we can study the system response to step disturbances to find when (after how many samples) the failure limits are reached for different error magnitudes 5 .…”

Section: Step Response Analysismentioning

confidence: 99%

“…This means that recovery must have been initiated before ¼ ¼¾ s after occurrence of the error. 5 For a fault that manifests as a step with magnitude Ñ on state Þ occurring at time Ø ¼, i.e., Þ Ñ ´ µ, the effect on the process output will be described by Ý´ µ Using this equation we can find the first sample (if any), , at which the requirements are no longer fulfilled, that is, the time within which the system needs to be recovered to avoid system failures. 6 For an error occurring in the most significant bit (i.e., with magnitude ¾ ¿½ ¡ ¾ ¾¿ ¾ ), the control error will be (due to the linearity): ¼ ¼ ¿ ¡ ¾ ½¾ , i.e., much higher.…”

Section: Step Response Analysismentioning

confidence: 99%

“…Methods for analyzing the effects of timing errors on control systems were presented in, e.g., [4,8]. Analysis of effects on system stability of data errors caused by EMI bursts was investigated in [5]. However, as catastrophic failures in safety-critical system may occur before the system reaches instability, we base our definition of system failure on thresholds of the magnitude and duration of the control error, i.e., the difference between the reference (desired) value of a controlled physical process property and the actual value of this property (e.g., in a system controlling compartment temperature in a car, the control error would be the difference between the temperature the driver has requested and the actual temperature measured by the sensors).…”

Section: Introductionmentioning

confidence: 99%

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A control theory approach for analyzing the effects of data errors in safety-critical control systems

Askerdal

Gäfvert²,

Hiller³

et al.

2002 Pacific Rim International Symposium on Dependable Computing, 2002. Proceedings.

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Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach

Elhoushy,

Zalam,

Sayed

et al. 2023

Biomed. Eng. Lett.

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Regulating blood glucose level (BGL) for type-1 diabetic patient (T1DP) accurately is very important issue, an uncontrolled BGL outside the standard safe range between 70 and 180 mg/dl results in dire consequences for health and can significantly increase the chance of death. So the purpose of this study is to design an optimized controller that infuses appropriate amounts of exogenous insulin into the blood stream of T1DP proportional to the amount of obtained glucose from food. The nonlinear extended Bergman minimal model is used to present glucose-insulin physiological system, an interval type-2 fuzzy logic controller (IT2FLC) is utilized to infuse the proper amount of exogenous insulin. Superiority of IT2FLC in minimizing the effect of uncertainties in the system depends primarily on the best choice of footprint of uncertainty (FOU) of IT2FLC. So a comparison includes four different optimization methods for tuning FOU including hybrid grey wolf optimizer-cuckoo search (GWOCS) and fuzzy logic controller (FLC) method is constructed to select the best controller approach. The effectiveness of the proposed controller was evaluated under six different scenarios of T1DP using Matlab/Simulink platform. A 24-h scenario close to real for 100 virtual T1DPs subjected to parametric uncertainty, uncertain meal disturbance and random initial condition showed that IT2FLC accurately regulate BGL for all T1DPs within the standard safe range. The results indicated that IT2FLC using GWOCS can prevent side effect of treatment with blood-sugar-lowering medication. Also stability analysis for the system indicated that the system operates within the stability region of nonlinear system.

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Effects of electromagnetic interference on controller-computer upsets and system stability

Cited by 22 publications

References 2 publications

A Maintenance-Oriented Fault Model for the DECOS Integrated Diagnostic Architecture

A Maintenance-Oriented Fault Model for the DECOS Integrated Diagnostic Architecture

A control theory approach for analyzing the effects of data errors in safety-critical control systems

Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach

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