Arbitrary‐order sliding mode‐based robust control algorithm for the developing artificial pancreas mechanism

In Diabetes Mellitus, the pancreas remains incapable of insulin administration that leads to hyperglycaemia, an escalated glycaemic concentration, which may stimulate many complications. To circumvent this situation, a closed‐loop control strategy is much needed for the exogenous insulin infusion in diabetic patients. This closed‐loop structure is often termed as an artificial pancreas that is generally established by the employment of different feedback control strategies. In this work, the authors have proposed an arbitrary‐order sliding mode control approach for development of the said mechanism. The term, arbitrary, is exercised in the sense of its applicability to any n ‐order controllable canonical system. The proposed control algorithm affirms the finite‐time effective stabilisation of the glucose–insulin regulatory system, at the desired level, with the alleviation of sharp fluctuations. The novelty of this work lies in the sliding manifold that incorporates indirect non‐linear terms. In addition, the necessary discontinuous terms are filtered‐out once before its employment to the plant, i.e. diabetic patient. The robustness, in the presence of external disturbances, i.e. meal intake is confirmed via rigorous mathematical stability analysis. In addition, the effectiveness of the proposed control strategy is ascertained by comparing the results with the standard literature.Inspec keywords: diseases, blood, control system synthesis, medical control systems, feedback, sugar, stability, closed loop systems, robust control, variable structure systemsOther keywords: finite‐time effective stabilisation, glucose–insulin regulatory system, sliding manifold, nonlinear terms, necessary discontinuous terms, employment, diabetic patient, arbitrary‐order sliding mode‐based robust control algorithm, developing artificial pancreas mechanism, Diabetes Mellitus, insulin administration, escalated glycaemic concentration, closed‐loop control strategy, exogenous insulin infusion, closed‐loop structure, different feedback control strategies, mode control approach, n‐order controllable canonical system, control algorithm affirms     

Non‐linear feedback control of the p 53 protein–mdm 2 inhibitor system using the derivative‐free non‐linear Kalman filter

It is proven that the model of the p 53–mdm 2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p 53 protein at the desirable levels. To estimate the non‐measurable elements of the state vector describing the p 53–mdm 2 system dynamics, the derivative‐free non‐linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p 53–mdm 2 system, the derivative‐free non‐linear Kalman filter is re‐designed as a disturbance observer. The derivative‐free non‐linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non‐linear model. The proposed non‐linear feedback control and perturbations compensation method for the p 53–mdm 2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.Inspec keywords: proteins, molecular biophysics, biochemistry, Kalman filters, inverse problems, perturbation theoryOther keywords: nonlinear feedback control, p53 protein‐mdm2 inhibitor system, derivative‐free nonlinear Kalman filter, differential flatness theory, protein synthesis loop, diffeomorphism, protein synthesis model, feedback control law, nonmeasurable elements, modelling uncertainties, inverse transformation, nonlinear model, perturbation compensation method, chemotherapy schemes, medication infusion     

Developing a model‐based sampling plan

Statistical process modeling is widely used in industry for forecasting the production outcomes, for process control and for process optimization. Applying a prediction model in a production process allows the user to calibrate/predict the mean of the distribution of the process outcomes and to partition the overall variation in the distribution of the process outcomes into explained (by the model) and unexplained (residuals) variations; thus, reducing the unexplained variability. The additional information about the process behavior can be used prior to the sampling procedure and may help to reduce the required sample size to classify a lot. This research focuses on the development of a model‐based sampling plan based ontextitCpk (process capability index). It is an extension of a multistage acceptance sampling plan also based on Cpk (Negrin et al., Quality Engineering 2009; 21 :306–318; Quality and Reliability Engineering International 2011; 27 :3–14). The advantage of this sampling plan is that the sample size needed depends directly and quantitatively on the quality of the process (Cpk), whereas other sampling plans such as MIL‐STD‐414 (Sampling Procedures and Tables for Inspection by Variables for Percent Defective, Department of Defense, Washington, DC, 1957.) use only qualitative measures. The objective of this paper is to further refine the needed sample size by using a predictive model for the lot's expectation. We developed model‐based sample size formulae which depend directly on the quality of the prediction model (as measured by R²) and adjust the ‘not model‐based’ multistage sampling plan developed in Negrin et al. (Quality Engineering 2009; 21 :306–318; Quality and Reliability Engineering International 2011; 27 :3–14) accordingly. A simulation study was conducted to compare between the model‐based and the ‘not model‐based’ sampling plans. It is found that when R² = 0, the model‐based and ‘not model‐based’ sampling plans require the same sample sizes in order to classify the lots. However, as R² becomes larger, the sample size required by the model‐based sampling plan becomes smaller than the one required by the ‘not model‐based’ sampling plan. In addition, it is found that the reduction of the sample size achieved by the model‐based sampling plan becomes more significant as Cpk tends to 1 and can be achieved without increasing the proportion of the classification errors. Finally, the suggested sampling plan was applied with areal data set from a chemicals manufacturing process for illustration. Copyright © 2011 John Wiley & Sons, Ltd.     

Chattering‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control

In this study, a closed‐loop control scheme is proposed for the glucose–insulin regulatory system in type‐1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose–insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well‐studied. Higher‐order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non‐linear property of glucose–insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro‐fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG‐level tracking and chattering reduction in the presence of daily glucose‐level disturbances is verified.Inspec keywords: fuzzy control, variable structure systems, particle swarm optimisation, neurocontrollers, fuzzy neural nets, blood, genetic algorithms, closed loop systems, medical control systems, fuzzy reasoning, diseases, nonlinear control systems, sugarOther keywords: data fusion, adaptive neuro‐fuzzy inference systems, particle swarm optimisation, hybrid controller, desired BG‐level tracking, chattering reduction, daily glucose‐level disturbances, closed‐loop control scheme, glucose–insulin regulatory system, type‐1 diabetic mellitus patients, innovative hybrid glucose–insulin regulators, artificial intelligence, fuzzy logic, genetic algorithm, Palumbo model, blood glucose level, T1DM patients, glucose reference tracking, insulin injection, mode controllers, glucose–insulin mechanism, chattering‐free hybrid adaptive neuro‐fuzzy inference system, particle swarm optimisation data fusion‐based BG‐level control     

Investigation of graphene‐on‐metal substrates for SPR‐based sensor using finite‐difference time domain

In this study, the authors investigated the effects of a single layer graphene as a coating layer on top of metal thin films such as silver, gold, aluminum and copper using finite‐difference time domain method. To enhance the resolution of surface plasmon resonance (SPR) sensor, it is necessary to increase the SPR reflectivity and decrease the full‐width‐half maximum (FWHM) of the SPR curve so that there is minimum uncertainty in the determination of the resonance dip. Numerical data was verified with analytical and experimental data where all the data were in good agreement with resonance angle differing in <10% due to noise present in components such as humidity and temperature. In further analysis, reflectivity and FWHM were compared among four types of metal with various thin film thicknesses where graphene was applied on top of the metal layers, and data was compared against pure conventional metal thin films. A 60 nm‐thick Au thin film results in higher performance with reflectivity of 92.4% and FWHM of 0.88° whereas single layer graphene‐on‐60 nm‐thick Au gave reflectivity of 91.7% and FWHM of 1.32°. However, a graphene‐on‐40 nm‐thick Ag also gave good performance with narrower FWHM of 0.88° and reflection spectra of 89.2%.Inspec keywords: graphene, surface plasmon resonance, finite difference time‐domain analysis, reflectivity, metallic thin films, silver, gold, aluminium, copper, chemical sensors, biological techniquesOther keywords: graphene‐on‐metal substrates, SPR‐based sensor, finite‐difference time domain, metal thin films, surface plasmon resonance sensor, SPR curve, resonance angles, reflectivity, C, Ag, Au, Al, Cu     

Process monitoring for correlated gamma‐distributed data using generalized‐linear‐model‐based control charts

A model‐based scheme is proposed for monitoring multiple gamma‐distributed variables. The procedure is based on the deviance residual, which is a likelihood ratio statistic for detecting a mean shift when the shape parameter is assumed to be unchanged and the input and output variables are related in a certain manner. We discuss the distribution of this statistic and the proposed monitoring scheme. An example involving the advance rate of a drill is used to illustrate the implementation of the deviance residual monitoring scheme. Finally, a simulation study is performed to compare the average run length (ARL) performance of the proposed method to the standard Shewhart control chart for individuals. Copyright © 2003 John Wiley & Sons, Ltd.     

Observer‐based resilient finite‐time control of blood gases model during extra‐corporeal circulation

This study aims at designing an observer‐based resilient controller to regulate the amount of oxygen and carbon dioxide in the blood of patients during the extra‐corporeal blood circulation process. More precisely, in this study, a suitable observer‐based resilient controller is constructed to regulate the levels of patient blood gases in a finite interval of time. The finite‐time boundedness with the prescribed H∞ performance index of the considered blood gases control system against modelling uncertainty and external disturbances is ensured by using Lyapunov stability analysis. Moreover, a set of sufficient conditions for obtaining the controller gain is developed in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed robust finite‐time control scheme is verified through simulation results. The result reveals that the blood gases are maintained in their physiological ranges during a stable extra‐corporeal circulation process via the proposed observer‐based resilient controller.Inspec keywords: blood, haemodynamics, oxygen, carbon compounds, controllers, medical control systems, biomedical equipment, Lyapunov methods, linear matrix inequalitiesOther keywords: observer‐based resilient finite‐time control, observer‐based resilient controller, oxygen amount, carbon dioxide amount, extracorporeal blood circulation process, patient blood gas levels, finite time interval, finite‐time boundedness, H_∞ performance index, blood gases control system, Lyapunov stability analysis, controller gain, linear matrix inequalities, physiological ranges, LMIs, CO₂ , O₂      

Sliding mode control for a fractional‐order non‐linear glucose‐insulin system

By providing the generalisation of integration and differentiation, and incorporating the memory and hereditary effects, fractional‐order modelling has gotten significant attention in the past few years. One of the extensively studied and utilised models to describe the glucose–insulin system of a human body is Bergman''s minimal model. This non‐linear model comprises of integer‐order differential equations. However, comparison with the experimental data shows that the fractional‐order version of Bergman''s minimal model is a better representative of the glucose–insulin system than its original integer‐order model. To design a control law for an artificial pancreas for a diabetic patient using a fractional‐order model, different techniques, including feedback linearisation, have been applied in the literature. The authors’ previous work shows that the fractional‐order version of Bergman''s model describes the glucose–insulin system in a better way than the integer‐order model. This study applies the sliding mode control technique and then compares the obtained simulation results with the ones obtained using feedback linearisation.Inspec keywords: nonlinear control systems, feedback, variable structure systems, differential equations, medical control systems, diseases, control system synthesis, sugar, nonlinear dynamical systemsOther keywords: fractional‐order nonlinear glucose‐insulin, hereditary effects, fractional‐order modelling, extensively, utilised models, glucose–insulin system, Bergman''s minimal model, nonlinear model, integer‐order differential equations, fractional‐order version, original integer‐order model, fractional‐order model, Bergman''s model, sliding mode control technique     

Design of regression model‐based automatic process control with reduced adjustment frequency

Automatic process control based on a regression model has been adopted as one of the important techniques to improve product quality in manufacturing processes. Though more frequent adjustments generally produce a superior control performance, it may also increase control cost and impair control applicability. In this paper, the concepts of quality margin and self‐compensation of noise change are introduced. Based on these concepts, a control strategy is proposed which is capable of ensuring an acceptable process performance with a reduced adjustment frequency. A case study of leaf spring forming process is conducted to compare the control performance and control adjustment frequency between the proposed approach and the existing methods. Some properties of the proposed control law are also studied. The proposed method is implemented in a hot steel rolling process to demonstrate the applicability of the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.     

Robust adaptive Lyapunov‐based control of hepatitis B infection

A new robust adaptive controller is developed for the control of the hepatitis B virus (HBV) infection inside the body. The non‐linear HBV model has three state variables: uninfected cells, infected cells and free viruses. A control law is designed for the antiviral therapy such that the volume of infected cells and the volume of free viruses are decreased to their desired values which are zero. One control input represents the efficiency of drug therapy in inhibiting viral production and the other control input represents the efficiency of drug therapy in blocking new infection. The proposed controller ensures the stability and robust performance in the presence of parametric and non‐parametric uncertainties (and/or bounded disturbances). The global stability and tracking convergence of the process are investigated by employing the Lyapunov theorem. The performance of the proposed controller is evaluated using simulations by considering different levels of uncertainties. Based on the obtained results, the proposed strategy can achieve its desired objectives with different cases of uncertainties.Inspec keywords: medical control systems, drugs, drug delivery systems, cellular biophysics, microorganisms, diseases, Lyapunov methods, adaptive controlOther keywords: robust adaptive Lyapunov‐based control, robust adaptive controller, hepatitis B virus infection, uninfected cells, free viruses, antiviral therapy, drug therapy, viral production, global stability, tracking convergence     

Architecture‐dependent robustness in a class of multiple positive feedback loops

Many types of multiple positive feedbacks with each having potentials to generate bistability exist extensively in natural, raising the question of why a particular architecture is present in a cell. In this study, the authors investigate multiple positive feedback loops across three classes: one‐loop class, two‐loop class and three‐loop class, where each class is composed of double positive feedback loop (DPFL) or double negative feedback loop (DNFL) or both. Through large‐scale sampling and robustness analysis, the authors find that for a given class, the homogeneous DPFL circuit (i.e. the coupled circuit that is composed of only DPFLs) is more robust than all the other circuits in generating bistable behaviour. In addition, stochastic simulation shows that the low stable state is more robust than the high stable state in homogeneous DPFL whereas the high‐stable state is more robust than the low‐stable state in homogeneous DNFL circuits. It was argued that this investigation provides insight into the relationship between robustness and network architecture.Inspec keywords: cellular biophysics, feedback, sampling methods, stochastic processesOther keywords: network architecture, low stable state, stochastic simulation, bistable behaviour, homogeneous DPFL circuit, robustness analysis, large‐scale sampling, DNFL, double negative feedback loop, double positive feedback loop, three‐loop class, two‐loop class, one‐loop class, cell architecture, bistability, multiple positive feedback loops, architecture‐dependent robustness     

Colorimetric‐based detection of Ureaplasma urealyticum using gold nanoparticles

Ureaplasma urealyticum (uu) is one of the most common agents of urogenital infections and is associated with complications such as infertility, spontaneous abortion and other sexually transmitted diseases. Here, a DNA sensor based on oligonucleotide target‐specific gold nanoparticles (AuNPs) was developed, in which the dispersed and aggregated states of oligonucleotide‐functionalised AuNPs were optimised for the colorimetric detection of a polymerase chain reaction (PCR) amplicon of U. urealyticum DNA. A non‐cross‐linking approach utilising a single Au‐nanoprobe specific of the urease gene was utilised and the effect of a PCR product concentration gradient evaluated. Results from both visual and spectral analyses showed that target–Au‐nanoprobe hybrids were stable against aggregation after adding the inducer. Furthermore, when a non‐target PCR product was used, the peak position shifted and salt‐induced aggregation occurred. The assay''s limit of detection of the assay was 10 ng with a dynamic range of 10–60 ng. This procedure provides a rapid, facile and low‐cost detection format, compared to methods currently used for the identification of U. urealyticum.Inspec keywords: patient diagnosis, diseases, enzymes, nanosensors, microorganisms, molecular biophysics, DNA, nanoparticles, aggregation, cellular biophysics, colorimetry, genetics, gold, nanomedicineOther keywords: urogenital infections, infertility, spontaneous abortion, sexually transmitted diseases, DNA sensor, oligonucleotide target‐specific gold nanoparticles, oligonucleotide‐functionalised AuNPs, colorimetric detection, polymerase chain reaction amplicon, noncross‐linking approach, single Au‐nanoprobe specific, urease gene, visual analyses, spectral analyses, target–Au‐nanoprobe hybrids, nontarget PCR product, salt‐induced aggregation, rapid cost detection format, facile cost detection format, low‐cost detection format, PCR product concentration, Ureaplasma urealyticum DNA, Au     

On‐chip label‐free impedance‐based detection of antibiotic permeation

Biosensors are analytical tools used for the analysis of biomaterial samples and provide an understanding about the biocomposition, structure, and function of biomolecules and/or biomechanisms by converting the biological response into an electrical and/or optical signal. In particular, with the rise in antibiotic resistance amongst pathogenic bacteria, the study of antibiotic activity and transport across cell membranes in the field of biosensors has been gaining widespread importance. Herein, for the rapid and label‐free detection of antibiotic permeation across a membrane, a microelectrode integrated microfluidic device is presented. The integrated chip consists of polydimethylsiloxane based microfluidic channels bonded onto microelectrodes on‐glass and enables us to recognize the antibiotic permeation across a membrane into the model membranes based on electrical impedance measurement, while also allowing optical monitoring. Impedance testing is label free and therefore allows the detection of both fluorescent and non‐fluorescent antibiotics. As a model membrane, Giant Unilamellar Vesicles (GUVs) are used and impedance measurements were performed by a precision inductance, capacitance, and resistance metre. The measured signal recorded from the device was used to determine the change in concentration inside and outside of the GUVs. We have found that permeation of antibiotic molecules can be easily monitored over time using the proposed integrated device. The results also show a clear difference between bilayer permeation that occurs through the lipidic bilayer and porin‐mediated permeation through the porin channels inserted in the lipid bilayer.     

Optimisation of food grade mixed surfactant‐based l‐ascorbic acid nanoemulsions using response surface methodology

Co‐surfactant free l‐ascorbic acid (LAA) nanoemulsions were prepared using mixed surfactants (Soya lecithin and Tween 80). Response surface methodology (RSM) was used to optimise the emulsifying conditions for LAA nanoemulsions. The effects of water proportion (6%–14% w/w), homogenisation pressure (80–160 MPa), surfactant concentrations (4%–12% w/w) and laa concentration (0.5–1.3 w/w) on responses (size of droplets and nanoemulsion stability) were investigated. RSM results showed that the values of responses can be successfully predicted through second‐order polynomial model. The coefficients of determinations for droplet size and nanoemulsion stability were 0.9375 and 0.9027, respectively. The optimum preparation conditions for l‐LAA nanoemulsion were 9.04% water proportion, 114.48 MPa homogenisation pressure, 7.36% surfactant concentration and 1.09% LAA concentration. At the end of one month storage study, the retention of LAA in optimised nanoemulsions stored at 4°C and 25°C were 74.4% and 66.7%, respectively. These results may provide valuable contributions for food and pharmaceutical industry to develop delivery system for food additives and nutraceutical components.     

Monitoring dependent process steps using robust cause‐selecting control charts

Cause‐selecting control charts are believed to be invaluable for monitoring and diagnosing multistage processes where the output quality of some stages is significantly impacted by the output quality of preceding stages. To establish a relationship between input and output variables, a standard procedure uses historical data, which are often prone to hold outliers. The presence of outliers tends to decrease the effectiveness of monitoring procedures because the regression model is distorted and the control limits become stretched. To dampen the negative repercussions of outliers, robust fitting techniques based on M‐estimators are implemented instead of the ordinary least‐squares method and two robust monitoring approaches are presented. An example is given to illustrate the application and performance of the proposed control charts. Furthermore, a simulation‐based study is included to investigate and compare the average run length of robust and non‐robust schemes. The results reveal that the robust procedure far outperforms the non‐robust counterpart due to its prompt detection of out‐of‐control conditions when outliers exist. Copyright © 2009 John Wiley & Sons, Ltd.     

A model‐based approach for attribute gauge analysis

A new method is proposed for the attribute gauge study. A mathematical model based on the Gauge Performance Curve (GPC) is introduced and the connection between the model parameters and gauge characteristics is discussed. The ability of the whole measurement system (the process and the gauge together) is characterized by two key probabilities: the probability of a rejected part being good and the probability of an accepted part being bad. These probabilities can be calculated either with the proposed mathematical model of GPC (GPC‐method) or by using relative frequencies (AIAG‐type method). The performance of the two approaches has been compared, and the proposed GPC‐method is proven to be statistically favourable. Copyright © 2010 John Wiley & Sons, Ltd.     

Selecting control factor values in a robust design using a scatter plot

When the results of an experimental design are analyzed, in which control factors and noise factors are involved, it may be difficult to determine the combination of values of the control factors that produce the best behavior of the response, addressing both its level (or distance from the target value) and its variability. This article presents an analysis proposal that is based on the model obtained for the response and uses, as its central element, a scatter plot of its expected value vs its standard deviation. In this plot, each point corresponds to a combination of values of the control factors; thus, it is easy to identify the points with better response behavior. In our opinion, this graph provides significant advantages over the other methods that have been proposed; among them is the fact that it is always a scatter plot, regardless of the number of factors that end up being active, and that it is easy to understand and use, especially with the possibilities offered by the current statistical software packages. Copyright © 2010 John Wiley & Sons, Ltd.     

Absolute voltage amplification using dynamic feedback control

A new principle of voltage amplification that is insensitive to drift and aging effects of the feedback resistors is presented. The effect of mismatch of these resistors is reduced to a second-order effect which results in a total gain error of less than 5×10^-6 without the need for any calibration. Moreover, the long-term stability of the whole amplifier is guaranteed by the design when used as a chopper amplifier