Biological pest control using a model‐based robust feedback

Biological control is the artificial manipulation of natural enemies of a pest for its regulation to densities below a threshold for economic damage. The authors address the biological control of a class of pest population models using a model‐based robust feedback approach. The proposed control framework is based on a recursive cascade control scheme exploiting the chained form of pest population models and the use of virtual inputs. The robust feedback is formulated considering the non‐linear model uncertainties via a simple and intuitive control design. Numerical results on three pest biological control problems show that the proposed model‐based robust feedback can regulate the pest population at the desired reference via the manipulation of a biological control action despite model uncertainties.Inspec keywords: cascade control, control system synthesis, nonlinear control systems, feedback, robust control, pest control, manipulatorsOther keywords: biological pest control, artificial manipulation, natural enemies, pest population models, robust feedback approach, recursive cascade control scheme, nonlinear model uncertainties, simple control design, intuitive control design, pest biological control problems, biological control action     

Bayesian tolerance interval control limits for attributes

The probability content of standard control limits for attributes can vary because distribution parameters that appear in the control limits are estimated based on previous data. This paper proposes using Bayesian tolerance interval control limits which control the probability content at a specified level with a given confidence. Bayesian tolerance interval control limits are developed for np, p, c and u charts and are illustrated with four examples from the literature. Moreover, Bayesian tolerance interval control limits can be used for processes at start‐up. Published in 2002 by John Wiley & Sons, Ltd.     

Novel algebraic meal disturbance estimation based adaptive robust control design for blood glucose regulation in type 1 diabetes patients

This study designs a robust closed‐loop control algorithm for elevated blood glucose level stabilisation in type 1 diabetic patients. The control algorithm is based on a novel control action resulting from integrating algebraic meal disturbance estimator with back‐stepping integral sliding mode control (BISMC) technique. The estimator shows finite time convergence leading to accurate and fast estimation of meal disturbance. Moreover, compensation of the estimated disturbance in controller provides significant reduction in chattering phenomenon, which is inherent drawback of sliding mode control (SMC). The controller is applied to one of the most reliable models of type 1 diabetic patients, named Bergman''s minimal model. The effectiveness and superiority of the designed controller is shown by comparing it to classical SMC and super‐twisting sliding mode control. The designed controller is subject to three different cases for detailed analysis of the controller''s robustness against meal disturbance. The three cases considered are hyperglycaemia, hyperglycaemia combined with meal disturbance and three meal disturbance. The simulation results confirm superior performance of algebraic disturbance estimator based BISMC controller for all the cases mentioned above.Inspec keywords: closed loop systems, robust control, sugar, medical control systems, variable structure systems, control system synthesis, blood, nonlinear control systems, adaptive control, diseasesOther keywords: adaptive robust control design, blood glucose regulation, type 1 diabetes patients, closed‐loop control algorithm, elevated blood glucose level stabilisation, type 1 diabetic patients, novel control action, algebraic meal disturbance estimator, mode control technique, accurate estimation, estimated disturbance, super‐twisting sliding mode control, algebraic disturbance estimator, BISMC controller, algebraic meal disturbance estimation, back‐stepping integral sliding mode control technique     

Generischer Steuerungs-Algorithmus zur Erstellung von Modulen für die Steuerungssoftware von ortsfesten Materialflusssystemen

Automated material handling systems are increasingly used in industry due to short process times and high throughputs. These systems have to be custom-built in order to utilize their full potential and therefore require individually created control software. To create this individual control software extensive manual programming is required, since a modular structure of the control algorithm with predefined modules is often missing. We fill this gap and describe such a modular control structure, which is based upon a two-layer architecture separating the hardware control from the material flow control. Our control structure uses a five-phase generic control algorithm, which represents a template by which the modules can be worked out. We describe this five-phase generic control algorithm, implement it for a demonstrator scenario and show its proper functionality. We show that with our two-layer architecture and the generic control algorithm predefined modules for the conveyor hardware and material flow elements can be created and then assembled in the needed configuration. Therefore, the presented method allows to create the control software for a custom-built material handling system by arranging predefined modules, thus without manual programming.     

A mixed control chart to monitor the process

In this paper, we propose a mixed control chart to monitor the process quality using attribute data combined with variable data. The proposed control chart proceeds like an np control chart based on the number of nonconforming parts but requires variable data only when the decision is indeterminate. The control coefficients are determined by considering the in-control and the out-of-control average run lengths for various specified parameters. The extensive tables are provided for the industrial use. The advantages of the proposed control chart are discussed over the traditional np control chart.     

Conditional Control Charts for Monitoring the Weibull Shape Parameter under Progressively Type II Censored Data

In this paper, (i) we propose new conditional Shewhart‐type control charts for monitoring the shape parameter of the Weibull distribution under a progressively type II censoring strategy, and (ii) we generalize the control charts proposed by Guo and Wang¹ for the progressively type II censoring case. We provide a comparison between these control charts in terms of the out‐of‐control average run length obtained by simulation for both the known and unknown parameter cases. A real example consisting of data from breaking stress of carbon fibers is also presented for illustration and comparison of the proposed control charts. Copyright © 2014 John Wiley & Sons, Ltd.     

Design of dual hormone blood glucose therapy and comparison with single hormone using MPC algorithm

The complete automated control and delivery of insulin and glucagon in type 1 diabetes is the developing technology for artificial pancreas. This improves the quality of life of a diabetic patient with the precise infusion. The amount of infusion of these hormones is controlled using a control algorithm, which has the prediction property. The control algorithm model predictive control (MPC) predicts one step ahead and infuses the hormones continuously according to the necessity for the regulation of blood glucose. In this research, the authors propose a MPC control algorithm, which is novel for a dual hormone infusion, for a mathematical model such as Sorenson model, and compare it with the insulin alone or single hormone infusion developed with MPC. Since they aim for complete automatic control and regulation, unmeasured disturbances at a random time are integrated and the performance evaluation is projected through statistical analysis. The blood glucose risk index (BGRI) and control variability grid analysis (CVGA) plot gives the additional evaluation for the comparative results of the two controllers claiming 88% performance by dual hormone evaluated through CVGA plot and 2.05 mg/dl average tracking error, 2.20 BGRI. The MPC developed for dual hormone significantly performs better and the time spent in normal glycaemia is longer while eliminating the risk of hyperglycaemia and hypoglycaemia.Inspec keywords: sugar, diseases, statistical analysis, biochemistry, blood, predictive control, patient treatment, artificial organsOther keywords: dual hormone blood glucose therapy, insulin, glucagon, type 1 diabetes, artificial pancreas, diabetic patient, glucose level, model predictive control, MPC control algorithm, dual hormone infusion, single hormone infusion, complete automatic control, blood glucose risk index, control variability grid analysis plot, Sorenson model, statistical analysis, normal glycaemia, hyperglycaemia, hypoglycaemia     

Control charts for simultaneously monitoring of process mean and coefficient of variation with and without auxiliary information

A control chart is very useful to control assignable causes which detect the shifted process parameters (eg, mean and dispersion). Simultaneous monitoring of the process parameters is a well‐known approach utilized for the bilateral processes. In the current study, we proposed the blended control chart that monitors the process mean and process coefficient of variation simultaneously. Further, the sensitivity of control chart is enhanced by incorporating an auxiliary variable. We have utilized the concept of EWMA chart and also the log transformation to transform the distribution of sample coefficient of variation to the normal distribution for structuring a joint monitoring control chart. The performance comparison among proposed control charts is presented. On the basis of ARLs and SDRLs, several advantages of the proposed control charts are diagnosed. The empirical evidence is also provided to support proposed control chart with a real‐life dataset.     

Economic Design of Attribute Control Charts

This paper proposes a production model for the current control of the number of defectives by an np-chart. An algorithm for determining the most economic control parameters is presented. An exceedingly simple procedure is suggested for designing control plans which approximate in cost the most economic control plans. This procedure is applicable at the workshop level.     

A convenient solver for solving optimal control problems

Abstract

This paper focuses on the development of a solver for solving optimal control problems. A developed numerical optimal control module integrated with the Sequential Quadratic Programming method is introduced. An optimal control problem solver based on the proposed method is implemented to solve optimal control problems efficiently in engineering applications. In addition, a systematic procedure for solving optimal control problems by using the optimal control problem solver is also proposed. A time‐optimal benchmark problem presented in the literature is used to illustrate for the capability and facility of solving optimal control problems. The numerical results demonstrate the proposed method and the procedure suggested in this paper are helpful to engineers in solving optimal control problems in a systematic and efficient manner.     

A hybrid homogeneously weighted moving average control chart for process monitoring

Several modifications and enhancements to control charts in increasing the performance of small and moderate process shifts have been introduced in the quality control charting techniques. In this paper, a new hybrid control chart for monitoring process location is proposed by combining two homogeneously weighted moving average (HWMA) control charts. The hybrid homogeneously weighted moving average (HHWMA) statistic is derived using two smoothing constants λ₁ and λ₂ . The average run length (ARL) and the standard deviation of the run length (SDRL) values of the HHWMA control chart are obtained and compared with some existing control charts for monitoring small and moderate shifts in the process location. The results of study show that the HHWMA control chart outperforms the existing control charts in many situations. The application of the HHWMA chart is demonstrated using a simulated data.     

Performance Measures for ―X Charts with Asymmetric Control Limits

Theoretical and empirical justification is given for using asymmetric control limits for certain types of production processes. The following are also discussed: the sensitivity of the performance measures to the process and control parameters, the advantages and disadvantages of using asymmetric control limits, and the construction of tradeoff curves to characterize performance. The justification is given in terms of a collection of quantitative performance measures for ―X charts with asymmetric control limits. The performance measures quantify the false‐alarm frequency, the sensitivity to out‐of‐control conditions, and the resources required for sampling. Copyright © 2005 John Wiley & Sons, Ltd.     

A new strategy for Phase I analysis in SPC

The Phase I analysis in statistical process control usually includes a task of filtering out out‐of‐control data in the historical data set via control charting. The conventional procedure for this is an iterative procedure that first uses all the samples to set up initial trial control limits and discards all the ‘out‐of‐control’ samples accordingly, and then iteratively repeats the screening step on the remaining samples until no more ‘out‐of‐control’ samples are detected. For simplicity, the ‘out‐of‐control’ samples here refer to the samples with their monitoring statistics exceeding the trial control limits. It is found in this study that this procedure throws away too many useful in‐control samples. To overcome this drawback, we propose and study a new iterative procedure that discards only one ‘out‐of‐control’ sample (i.e. the most extreme one) at each iteration. Our simulation study, using the Shewhart X Chart for illustration, demonstrates that the new one‐at‐a‐time procedure reduces dramatically the occurrences of false alarms. For cost‐saving, we further suggest a new strategy on when to stop and inspect the process to look for assignable causes for samples signaling out‐of‐control alarms. To determine the control limits, both the traditional method that controls the individual false‐alarm‐rate and the Bonferroni method that controls the overall false‐alarm‐rate are considered. The performances of the proposed schemes are evaluated and compared in terms of the false‐alarm rate and the detecting power via simulation studies. Copyright © 2009 John Wiley & Sons, Ltd.     

An adaptive control strategy for robotic manipulators

Abstract

An adaptive control strategy is proposed for the gross motion control of general purpose industrial manipulators. The control structure consists of a self‐tuning variational control component and a feedforward compensation component. Two control schemes are derived and their performance in tracking accuracy and robustness is evaluated by computer simulation.     

Steady‐state ARL analysis of ARL‐unbiased EWMA‐RZ control chart monitoring the ratio of two normal variables

Very recently, control charts for monitoring the ratio of 2 normal variables have been investigated in statistical process control. In the two‐sided case, however, these control charts tend to be average run length (ARL) biased, in the sense that some out‐of‐control ARL values are larger than the in‐control ARL. This paper proposes an ARL‐unbiased EWMA control chart for monitoring of this kind of ratio with each subgroup consisting of n?1 sample units. Also, to study the long‐term properties of ARL‐unbiased EWMA‐RZ control chart, we investigate the steady‐state ARL. Several tables and figures are given to show the statistical properties of the proposed control charts. The comparison results show that the proposed ARL‐unbiased chart outperforms other two‐sided control charts in terms of the zero‐state and steady‐state ARL. An example illustrates the use of this chart on a real quality control problem from the food industry.     

Control of AC–DC grid side converter with single AC current sensor

Conventionally, two AC side current sensors are needed in vector control of grid side converter for AC–DC bidirectional power conversion. The present paper proposes a technique where the control can be achieved with the use of only one AC side current sensor. The control principle utilises the information of un-sensed second current sensor for its estimation, which is embedded and readily available in conventional control technique itself. In the proposed method, the grid side d–q axis reference currents of the current controllers are used for estimation of β-axis component of grid current, while the α-axis component of grid current is calculated by one AC side current sensor. Effect of voltage unbalance on the control is also studied in this paper. The proposed control is validated with detailed simulation and experimental observations for both steady-state and transient conditions. The proposed control gives satisfactory performance.     

An Individuals Generalized Likelihood Ratio Control Chart for Monitoring Linear Profiles

This paper investigates a generalized likelihood ratio (GLR) control chart for detecting sustained changes in the parameters of linear profiles when individual observations are sampled. The control charts usually used for monitoring linear profiles are based on taking a sample of n observations at each sampling time point, where n is large enough that a regression model can be fitted at each sampling point using these n observations. For this sampling scenario, it has been shown that a GLR control chart has many advantages over other control chart schemes in terms of convenience of design, fast detection of process changes, and useful diagnostic aids. However, in many applications, it may not be convenient or possible to take a sample larger than n = 1. Therefore, it is desirable to develop some control chart to monitor profile data with individual observations (n = 1) at each sampling point. In this paper, we consider a GLR control chart based on individual observations and show that it has certain advantages compared with the GLR chart based on groups of observations. An important advantage of GLR control charts is that the only design parameter that needs to be specified in order to use a GLR chart is the control limit, and here, control limits for linear profiles up to eight regression coefficients are provided for convenient use by practitioners. Copyright © 2013 John Wiley & Sons, Ltd.     

Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks

Most multivariate quality control procedures evaluate the in‐control or out‐of‐control condition based upon an overall statistic, like Hotelling's T². Although T² is optimal for finding a general shift in mean vectors, it is not optimal for shifts that occur for some subset of variables. This introduces a persistent problem in multivariate control charts, namely the interpretation of a signal that often discourages practitioners in applying them. In this paper, we propose an artificial neural network based model to diagnose faults in out‐of‐control conditions and to help identify aberrant variables when Shewhart‐type multivariate control charts based on Hotelling's T² are used. The results of the model implementation on two numerical examples and one case of real world data are encouraging. Copyright © 2005 John Wiley & Sons, Ltd.     

Multivariate statistical process control charts: an overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart‐type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out‐of‐control signal. Copyright © 2006 John Wiley & Sons, Ltd.     

Development of Run-To-Run (R2R) controller for the multiple-input multiple-output (MIMO) system using fuzzy control theories

Run-to-Run (R2R) control has been extensively applied in semiconductor manufacturing. In particular, del Castillo, E. and Rajagopal, R., A multivariate double EWMA process adjustment scheme for drifting processes. IIE Trans., 2002, 34, 1055–1068, investigated double multivariate exponentially weighted moving average (MEWMA) controller for the multiple-input multiple-output (MIMO) system in an attempt to adjust and maintain the linear dynamic process outputs on target. Multivariate fuzzy control, inherently different from conventional MEWMA-based control, is another promising alternative that consists of fuzzy logic and set concept. Providing the fuzzy control can structure an appropriate membership function for the R2R MIMO system, thus it can be shown a practically useful control tool in comparison to MEWMA control. In this paper, fuzzy logic is utilized to design the multivariate fuzzy controller for the type of R2R applications based primarily on the min-max-gravity method advocated by Gupta, M.M., Kiszka, J.B. and Trojan, G.M., Multivariable structure of fuzzy control systems. IEEE Trans. Sys., Man Cybern., 1986, 16, 638–656. Under a variety of disturbance models, the proposed multivariate fuzzy controller can produce quite competitive control performance when compared to MEWMA control.