Experimental validation of a resilient monitoring and control system

Complex, high performance, engineering systems have to be closely monitored and controlled to ensure safe operation and protect public from potential hazards. One of the main challenges in designing monitoring and control algorithms for these systems is that sensors and actuators may be malfunctioning due to malicious or natural causes. To address this challenge, this paper addresses a resilient monitoring and control (ReMAC) system by expanding previously developed resilient condition assessment monitoring systems and Kalman filter-based diagnostic methods and integrating them with a supervisory controller developed here. While the monitoring and diagnostic algorithms assess plant cyber and physical health conditions, the supervisory controller selects, from a set of candidates, the best controller based on the current plant health assessments. To experimentally demonstrate its enhanced performance, the developed ReMAC system is then used for monitoring and control of a chemical reactor with a water cooling system in a hardware-in-the-loop setting, where the reactor is computer simulated and the water cooling system is implemented by a machine condition monitoring testbed at Idaho National Laboratory. Results show that the ReMAC system is able to make correct plant health assessments despite sensor malfunctioning due to cyber attacks and make decisions that achieve best control actions despite possible actuator malfunctioning. Monitoring challenges caused by mismatches between assumed system component models and actual measurements are also identified for future work.     

Experimental validation of a model of an uncontrolled bicycle

In this paper, an experimental validation of some modelling aspects of an uncontrolled bicycle is presented. In numerical models, many physical aspects of the real bicycle are considered negligible, such as the flexibility of the frame and wheels, play in the bearings, and precise tire characteristics. The admissibility of these assumptions has been checked by comparing experimental results with numerical simulation results. The numerical simulations were performed on a three-degree-of-freedom benchmarked bicycle model. For the validation we considered the linearized equations of motion for small perturbations of the upright steady forward motion. The most dubious assumption that was validated in this model was the replacement of the tires by knife-edge wheels rolling without slipping (non-holonomic constraints). The experimental system consisted of an instrumented bicycle without rider. Sensors were present for measuring the roll rate, yaw rate, steering angle, and rear wheel rotation. Measurements were recorded for the case in which the bicycle coasted freely on a level surface. From these measured data, eigenvalues were extracted by means of curve fitting. These eigenvalues were then compared with the results from the linearized equations of motion of the model. As a result, the model appeared to be fairly accurate for the low-speed low-frequency behaviour.     

Experimental validation of a subject-specific maximum endurance time model

This study aimed at experimentally validating a subject-specific maximum endurance time (MET) model. Thirty health participants (15 males and 15 females; Age: mean = 21.5 years, SD = 1.6 years) volunteered to conduct an isometric elbow flexion task until exhaustion. The endurance times of each participant were measured under relative exertion levels ranging from 30% MVC (Maximum Voluntary Contraction) to 70% MVC at 10% intervals. Assessment of the model showed that the intensity–endurance time relationship for each studied individual could be well fitted by the subject-specific MET model (R² > 0.89). The fatigue rates identified from the model fitting were normally distributed (Mean = 0.96 min^?1, SD = 0.29 min^?1). In addition, the fatigue rates of the male group were significantly higher than the female group. The subject-specific MET model can be used to predict the MET for individual workers, and further support physical task design, based on the fatigability data of a targeted worker population.

Practitioner Summary: Ergonomists have extensively used MET models in physical fatigue assessment and physical task design. A subject-specific MET model could be used to predict the MET at individual levels, and also to support work design for a target worker population, based on the fatigability data distribution obtained from sampled workers.     

Data driven nonparametric identification and model based control of glucose-insulin process in type 1 diabetics

Closed loop control of glucose homeostasis via subcutaneous insulin infusion and continuous glucose monitoring system can give better living to a type 1 diabetic patient. This paper deals with the real time implementation of internal model control (IMC) of subcutaneous insulin infusion. The model based control is applied on the nonparametric model of the patient identified in real time from input–output data. Meal simulation model of the glucose-insulin system of type 1 diabetic patient based on the work of Dalla Man et. al. is considered. This model is constructed in hardware platform that acts as the virtual patient. The data-driven nonparametric model of the virtual patient is identified in real time by computing Volterra kernels. The kernels are solved up to second order using recursive least squares (RLS) algorithm with short memory length of M = 2. The validation results of the identified model output and the actual output have shown good fit in both simulation and real time environments. The frequency domain kernels are used in internal model control to generate insulin dosage. The control algorithm is developed in simulation and implemented in real time with hardware in loop on dSPACE platform. The closed loop system yields good meal disturbance rejection, less undershoots, settling time and more profoundly smaller requirement of insulin infusion as compared to the earlier reported data.     

Derivation and validation of a coal mill model for control

The paper presents development and validation of a coal mill model to be used for improved mill control, which may lead to a better load following capability of power plants fired by pulverized coal. The model is relatively simple, yet it captures all significant mill dynamics. The model is validated using data from four mills of a similar type from two different manufacturers. In the validation, model parameters are estimated using an efficient evolutionary algorithm called Differential Evolution. The model parameters are similar for all four mills, indicating that the model structure can be trusted.     

Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model

Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. This paper introduces a model capable of capturing the essential glucose and insulin kinetics in patients from retrospective data gathered in an intensive care unit (ICU). The model uses two time-varying patient specific parameters for glucose effectiveness and insulin sensitivity. The model is mathematically reformulated in terms of integrals to enable a novel method for identification of patient specific parameters. The method was tested on long-term blood glucose recordings from 17 ICU patients, producing 4% average error, which is within the sensor error. One-hour forward predictions of blood glucose data proved acceptable with an error of 2-11%. All identified parameter values were within reported physiological ranges. The parameter identification method is more accurate and significantly faster computationally than commonly used non-linear, non-convex methods. These results verify the model's ability to capture long-term observed glucose-insulin dynamics in hyperglycemic ICU patients, as well as the fitting method developed. Applications of the model and parameter identification method for automated control of blood glucose and medical decision support are discussed.     

Continuous-time control model validation using finite experimentaldata

The application of robust control theory requires models containing unknown, bounded perturbations and unknown, bounded input signals. Model validation is a means of assessing the applicability of a given model with respect to experimental data. This paper develops a theoretical framework, and a computational solution, for the model validation problem in the case where the model, including unknown perturbations and signals, is given in the continuous time domain, yet the experimental datum is a finite, sampled signal. The continuous nature of the unknown components is treated directly with a sampled data lifting theory. This gives results which are valid for any sample period and any datum length. Explicit calculation of whether sufficient data for invalidation has been obtained arises naturally in this framework. A common class of robust control models is treated and leads to a convex matrix optimization problem. A simulation example illustrates the approach     

基于可信验证的DBMS访问控制模型

针对目前访问控制模型在系统的安全实现方面存在的不足,在RABC的基础上,提出了可信操作环境下基于可信验证的DBMS访问控制模型,该模型满足系统的保密性和完整性需求,最大程度实现信息双向流动,同时支持最小特权安全特性,是一个权限分配灵活的访问控制模型。     

Applying a validation framework to a working airport terminal model

Validation is an important issue in the development and application of Bayesian Belief Network (BBN) models, especially when the outcome of the model cannot be directly observed. Despite this, few frameworks for validating BBNs have been proposed and fewer have been applied to substantive real-world problems. In this paper we adopt the approach by Pitchforth and Mengersen (2013), which includes nine validation tests that each focus on the structure, discretisation, parameterisation and behaviour of the BBNs included in the case study.We describe the process and result of implementing a validation framework on a model of a real airport terminal system with particular reference to its effectiveness in producing a valid model that can be used and understood by operational decision makers. In applying the proposed validation framework we demonstrate the overall validity of the Inbound Passenger Facilitation Model as well as the effectiveness of the validity framework itself.     

Using self-organizing maps to model turnover of sales agents in a call center

This paper proposes an approach for modeling employee turnover in a call center using the versatility of supervised self-organizing maps. Two main distinct problems exist for the modeling employee turnover: first, to predict the employee turnover at a given point in the sales agent's trial period, and second to analyze the turnover behavior under different performance scenarios by using psychometric information about the sales agents. Identifying subjects susceptible to not performing well early on, or identifying personality traits in an individual that does not fit with the work style is essential to the call center industry, particularly when this industry suffers from high employee turnover rates. Self-organizing maps can model non-linear relations between different attributes and ultimately find conditions between an individual's performance and personality attributes that make him more predisposed to not remain long in an organization. Unlike other models that only consider performance attributes, this work successfully uses psychometric information that describes a sales agent's personality, which enables a better performance in predicting turnover and analyzing potential personality profiles that can identify agents with better prospects of a successful career in a call center. The application of our model is illustrated and real data are analyzed from an outbound call center.     

Exploiting design patterns to automate validation of class invariants

In this paper, techniques are presented that exploit two design patterns, the Visitor pattern and the Decorator pattern, to validate invariants about the data attributes in a C++ class automatically. To investigate the pragmatics involved in using the two patterns, a study of an existing, well‐tested application, keystone, a parser and front‐end for the C++ language, is presented. Results from the study indicate that these two patterns provide flexibility in terms of the frequency and level of granularity of validation of the class invariants, which are expressed in the Object Constraint Language (OCL). The quantitative results measure the impact of these approaches and the additional faults uncovered through validation of the case study. Copyright © 2005 John Wiley & Sons, Ltd.     

Stability validation of a constrained model predictive networked control system with future input buffering

This paper addresses the stability of a newly-developed control strategy for networked control systems (NCS). This control strategy hones the potential of constrained model predictive control (MPC) by buffering the predicted control sequence at the actuator in anticipation of typical data transmission errors associated with NCS. Closed-loop stability in the sense of Lyapunov is guaranteed for the controller in the linear case, by bounding the projected receding horizon costs by lower- and upper-bounding terms using a predetermined terminal cost. A stability theorem is developed, which provides a suboptimal measure for the controller in real time, and is sufficient to estimate the worst-case transmission delay that can be handled by the developed control buffering strategy. The stability conditions, as governed by the theorem, are validated through real-time implementation on an electro-hydraulic servo system of an industrial processing machine, through an Ethernet network.     

Wavelet ANOVA approach to model validation

Model validation is that critical component in the simulation development process that ensures a model is truly representative of the system that it is meant to model. Although there are numerous validation techniques described in the literature, many of these techniques still require some amount of subjective analysis in order to assess validity. This is particularly true with dynamic simulation output. To reduce or eliminate this subjectivity, this paper proposes a validation process that uses wavelet analysis of variance (WANOVA) as an effective method to statistically accept or reject a model as valid. This WANOVA validation approach performs statistical inference in the time-frequency domain to take advantage of wavelet sparsity and decorrelation. This process uses a test statistic based on thresholded wavelet coefficients to test the null hypothesis that the set of system data and model data are statistically equivalent. The validation technique is illustrated using a simulation study and empirical data from an automobile crash study.     

A time-domain approach to model validation

In this paper we offer a novel approach to control-oriented model validation problems. The problem is to decide whether a postulated nominal model with bounded uncertainty is consistent with measured input-output data. Our approach directly uses time-domain input-output data to validate uncertainty models. The algorithms we develop are computationally tractable and reduce to (generally nondifferentiable) convex feasibility problems or to linear programming problems. In special cases, we give analytical solutions to these problems     

Knowledge-based approach to simulation model validation

Validation is an important part of simulation modeling. The shortcoming of traditional validation is lack of sufficient knowledge and lack of organization of the existing knowledge. This research explored the possibility of using knowledge from fields other than simulation, such as functional design and database design, for conceptual validation. Two simulation models were tested using the knowledge. An organization of all the knowledge into a knowledge-base was suggested.     

Experimental testing of flexible recipe control based on a hybrid model

Flexible recipe control is aimed at improving the performance of batch processes by changing the recipe parameters. Compensation is effected with the aid of the process model and optimisation. This strategy was used for the quality control of an industrial process in TiO₂ pigment production. The control algorithm was based on a hybrid model in order to use all the information about the process (knowledge, data, experience) and to design the model from a small number of real-plant experiments. When testing on the real process the algorithm was able to move the process output closer to the desired value.     

A multicompartment model for open-loop control of glucose in insulin-dependent diabetics

This paper describes a model that has been developed for individually adjusted therapy management in insulin-dependent diabetics. The multicompartment model considers all relevant aspects of glucose kinetics and its dependence on insulin. The structure of the model reflects that of the physiological system. Its parameters can be classified into (a) general parameters that are independent of the individual, (b) classifying parameters that are related systematically to the individual, and (c) distinguishing parameters. Classifying parameters allow a very convenient adjustment to relevant features of the individual like sex, age, body weight and length. The model can be employed in open-loop control for the calculation of insulin dosages. It allows the prediction of the system's behaviour as well as the consideration of predictable disturbance variables, e.g. food intake and physical exercise.     

Optimal control for chatter mitigation in milling—Part 2: Experimental validation

The productivity of milling operations in chatter-free conditions can be improved using active structural methods. This paper presents the use of a mechatronic system integrated into the spindle unit combined with two different optimal control strategies. The first one aims to minimize the influence of cutting forces on tool tip deviations. The second one explicitly considers the process interaction and attempts to stabilize the overall closed-loop system for specific machining conditions. The first part of this two-part paper describes the modeling and formulation used for both strategies. In this second part, the experimental validation of the proposed concept is presented.     

Specification and validation of a security policy model

The paper describes the development of a formal security policy model in Z for the NATO Air Command and Control System (ACCS): a large, distributed, multilevel-secure system. The model was subject to manual validation, and some of the issues and lessons in both writing and validating the model are discussed     

Experimental validation of electromagnetic software

While the last decade has seen an explosion in the availability of microwave software, experimental validation efforts have seen little effort. As a result, most published validation efforts are, justifiably, viewed with skepticism. Thus, when an analysis is to be validated, it must be validated again and again for, and by, each group of users, often at considerable expense. This article describes areas for improvement of validation techniques, with examples drawn from current literature. The ultimate objective in the field of experimental validation is to evaluate a test suite with such quality that multiple validations no longer need be performed by each user.