TYPE 1 DIABETIC PATIENT INSULIN DELIVERY USING ASYMMETRIC PI CONTROL

Type 1 diabetes is characterized by the destruction of the only insulin producing cells in the body. The typical course of action consists of daily insulin injections or an insulin pump. Assuming available methods for online monitoring of glucose concentrations, feedback control can be applied to this problem to improve regulation of glucose concentrations. A control algorithm is presented for feedback control of glucose levels in Type 1 patients. The control problem may be viewed as asymmetric, with negative variation from normal values treated with a more aggressive response than positive deviation. A simple asymmetric proportional-integral (PI) controller is presented where controller parameters vary depending on the sign of the current error value. Optimal closed-loop tuning parameters for the asymmetric control system are determined using local search methods. The asymmetric control system is then considered for robustness analysis using standard techniques from linear systems theory.     

TYPE 1 DIABETIC PATIENT INSULIN DELIVERY USING ASYMMETRIC PI CONTROL

Type 1 diabetes is characterized by the destruction of the only insulin producing cells in the body. The typical course of action consists of daily insulin injections or an insulin pump. Assuming available methods for online monitoring of glucose concentrations, feedback control can be applied to this problem to improve regulation of glucose concentrations. A control algorithm is presented for feedback control of glucose levels in Type 1 patients. The control problem may be viewed as asymmetric, with negative variation from normal values treated with a more aggressive response than positive deviation. A simple asymmetric proportional-integral (PI) controller is presented where controller parameters vary depending on the sign of the current error value. Optimal closed-loop tuning parameters for the asymmetric control system are determined using local search methods. The asymmetric control system is then considered for robustness analysis using standard techniques from linear systems theory.     

模糊自适应PI控制在空调系统中的研究

以空气焓差法试验台空调系统的温度控制系统为具体仿真对象建立了数学模型,该空调系统可以看作是一阶惯性加纯滞后的环节,而且对象的过程参数和时延时间是时变的,传统的PID控制无法获得理想的控制效果。提出了一种无需辨识环节的具有智能的模糊自适应PI的控制算法并将其应用在该空调系统中,该算法对模糊控制和PI控制进行有机结合,根据实际控制经验,通过模糊控制规则对控制回路中PI控制器的参数进行实时整定,并将该控制算法和经过良好整定的PI控制器在空调系统中的控制性能进行比较。仿真结果表明,模糊自适应PI控制提高了系统的鲁棒性、减小了超调量、提高了抗干扰能力、缩短了调整时间。     

Integrating data‐based modeling and nonlinear control tools for batch process control

A data‐based multimodel approach is developed in this work for modeling batch systems in which multiple local linear models are identified using latent variable regression and combined using an appropriate weighting function that arises from fuzzy c‐means clustering. The resulting model is used to generate empirical reverse‐time reachability regions (RTRRs) (defined as the set of states from where the data‐based model can be driven inside a desired end‐point neighborhood of the system), which are subsequently incorporated in a predictive control design. Simulation results of a fed‐batch reactor system under proportional‐integral (PI) control and the proposed RTRR‐based design demonstrate the superior performance of the RTRR‐based design in both a fault‐free and faulty environment. The data‐based modeling methodology is then applied on a nylon‐6,6 batch polymerization process to design a trajectory tracking predictive controller. Closed‐loop simulation results illustrate the superior tracking performance of the proposed predictive controller over PI control. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Detection and Diagnosis of Plant‐Wide Oscillations

This paper presents some emerging techniques for detection and root‐cause diagnosis of plant‐wide oscillations, and demonstrates their efficacy through a successful industrial case study. The recently proposed autocorrelation function based method (Thornhill et al., J. Proc. Control 13, 91–100, 2003a) is used for detection of oscillations in the process measurements. Signals having common oscillations are analyzed for the presence of valve stiction using higher order statistical methods (Choudhury et al., Automatica 40, 1719–1728, 2004b) . A method employing changes in controller gain is proposed for distinguishing an internally generated oscillation from an external oscillatory disturbance. This method of changing controller gain is used to confirm the presence of control valve stiction. The proposed techniques have been used successfully to identify the root cause of plant‐wide oscillations in an industrial case study using routine operating data.     

A comparative study of different types of controllers used for blood glucose regulation system

Maintaining the glucose concentration in normoglycemic range in Type I diabetic patients is challenging. In this study H_∞ control is applied for the insulin delivery to prevent the hyperglycaemic levels in a type I diabetic patient. A nonlinear model is linearized around nominal condition and reduced for control synthesis. H_∞ controller was compared with the two other types of controller and performance results shows evaluatory results.     

Nonlinear temperature control of a batch suspension polymerization reactor

This paper concerns nonlinear temperature control of a batch polymerization reactor where suspension polymerization of methyl methacrylate (MMA) takes place. For this purpose, four control algorithms, namely, a fix proportional‐integral (PI) controller, an adaptive proportional‐integral‐derivative (PID) controller and two globally linearizing control (GLC) schemes, one for known kinetic model (GLC‐I) and the other for unknown kinetic model (GLC‐II), are selected. The performances of these controllers are compared through simulation and real‐time studies in the presence of different levels of parameter uncertainty. The results indicate that GLCI and GLC‐II have better performances than fix PI and adaptive PID, especially in case of strong gel effect. The worst performance belongs to adaptive PID because of rapid model changes in gel effect region. GLC‐II has a simpler structure than GLC‐I and can be used without requiring the kinetic model. In implementation of GLC‐I the closed loop observer should be used because of model uncertainties.     

Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes

Accurate prediction of future blood glucose trends has the potential to significantly improve glycemic regulation in type 1 diabetes patients. A model‐based controller for an artificial β‐cell, for example, would determine the most efficacious insulin dose for the current sampling interval given available input–output data and model predictions of the resultant glucose trajectory. The two inputs most influential to the glucose concentration are bolused insulin and meal carbohydrates, which in practice are often taken simultaneously and in a specified ratio. This linear dependence has adverse effects on the quality of linear dynamic models identified from such data. On the other hand, inputs with greater degrees of excitation may force the subject into extreme hypoglycemia or hyperglycemia, and thus may be clinically unacceptable. Inputs with good excitation that do not endanger the subject are shown to result in models that can predict glucose trends reasonably accurately, 1–2 h ahead. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Glucose‐sensitive and blood‐compatible nanogels for insulin controlled release

For insulin delivery, many reported glucose‐sensitive materials are designed to response to the glucose in the blood. However, few particular studies on their blood compatibility have been reported. In this article, for controlled insulin release in diabetes therapy, a glucose‐sensitive nanogel was prepared through thermally initiated precipitation polymerization using the aminophenylboronic acid‐containing monomer to copolymerize with methacrylic acid. The obtained nanogels showed the uniform and spheroidal morphology as observed by SEM, and their sizes in aqueous solution are dependent on the concentration of glucose. Through in vitro and in vivo insulin release tests, it was found that nanogels showed the glucose‐dependent insulin release and prolonged effect of lowing blood glucose level. The blood compatibility of nanogels has also been explored through various assays including the hemolysis, activated partial thromboplastin time, prothrombin time as well as the thromboelastography. All results indicated that the obtained glucose‐sensitive nanogels showed good blood safety. Moreover, their low cytotoxicity suggested a potential application in diabetes therapy. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43504.     

Confirmation of control valve stiction in interacting systems

Stiction in a control valve generates limit cycles which can propagate to the connecting units, resulting in plant‐wide oscillations and impacting the overall process performance. For stiction confirmation, Choudhury et al. [Choudhury et al., CJChE 2007;85:208–219] introduced the controller gain change method, which is based on the change in the oscillation frequency due to changes in the controller gain. In this paper, we show that this method can fail to accurately isolate and confirm the presence of the sticky valve in interacting multi‐input multi‐output systems. We propose a strategy based on the magnitude of relative change in oscillation frequency due to changes in controller gain to overcome the limitations of the existing method. The effectiveness of the proposed method is demonstrated using simulation studies.     

Fuzzy Control Of A Fluidized Bed Dryer

Fluidized bed dryers are utilised in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for-process nonlinearities and exhibits more robust behaviour. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzy controller exhibits a remarkable dynamic behaviour, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.     

Adaptive type-2 fuzzy logic control of a bioreactor

Two adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI controller, where the output scaling factor is adjusted online by fuzzy rules according to the current trend of the controlled process. The performance of a type-2 fuzzy logic controller with 49 rules is used as reference.     

I.D.S'90, PRAGUE,CZECHOSLOVAKIA

ABSTRACT

Fluidized bed dryers are utilised in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for-process nonlinearities and exhibits more robust behaviour. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzy controller exhibits a remarkable dynamic behaviour, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.     

Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC.     

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Control of pH processes is very difficult due to nonlinear dynamics, high sensitivity at the neutral point, and changes in the concentrations of known or unknown chemical species. In this study, a dynamic fuzzy adaptive controller (DFAC) with a new inference mechanism is proposed and applied for the control of pH processes. The DFAC consists of a low-level basic control phase with a minimum rule base and a high-level dynamic learining phase with an updating mechanism to interact and modify the control rule base. The DFAC can self-adjust its fuzzy control rules using information from the process during on-line control and create new fuzzy control rules or modify the present control rules using its learning capability from past control trends. The controller is evaluated by applying it to a weak acid-strong base pH process with input disturbances and to another pH process that involve that has changes in acidic/buffering streams. The results of the DFAC with the new inference mechanism are compared with the known inference mechanisms, the fuzzy controller, the conventional PI controller, and also with an adaptive PID controller. The proposed DFAC provides better performance for set point tracking of the pH and rejection of load disturbances and buffering affects.     

Optimization‐Based Nonlinear Centralized Controller Tuning of Liquid‐Liquid Extraction Processes

Abstract

The control problem of an agitated contactor is considered in this work. A Scheibel extraction column is modeled using the non‐equilibrium backflow mixing cell model. Model dynamic analysis shows that this process is highly nonlinear, thus the control problem solution of such a system needs to tackle the process nonlinearity efficiently. The control problem of this process is solved by developing a multivariable nonlinear control system implemented in MATLAB?. In this control methodology, a new controller tuning method is adopted, in which the time‐domain control parameter‐tuning problem is solved as a constrained optimization problem. A MIMO (multi‐input multi‐output) PI controller structure is used in this strategy. The centralized controller uses a 2×2 transfer function and accounts for loops interaction. The controller parameters are tuned using an optimization‐based algorithm with constraints imposed on the process variables reference trajectories. Incremental tuning procedure is performed until the extractor output variables transient response satisfies a preset uncertainty which bounds around the reference trajectory. A decentralized model‐based IMC (internal model control) control strategy is compared with the newly developed centralized MIMO PI control one. Stability and robustness tests are applied to the two algorithms. The performance of the MIMO PI controller is found to be superior to that of the conventional IMC controller in terms of stability, robustness, loops interaction handling, and step‐change tracking characteristics.     

Application of genetic algorithm to self-organizing fuzzy controller in fed-batch culture ofScutellaria baicalensis G. plant cell

A self-organizing fuzzy controller is constructed for control of substrate concentration in fed-batch operation of a cell culture process. A genetic algorithm is used to generate fuzzy rules of the self-organizing fuzzy controller and to modify the universe of discourse automatically. The fuzzy controller is designed for the application ofScutellaria baicalensis G. plant cell culture process as a model system. A substrate feeding strategy of a two stage culture method to maximize flavone glycoside production in fed-batch culture ofScutellaria baicalensis G. plant cell is proposed based on structured model of growth and product synthesis. As a two stage culture, the feeding strategy consists of the first period at 22 g/L of glucose concentration to promote cell growth and the second period at 10 g/L of glucose concentration to promote flavone glycoside synthesis. The designed self-organizing fuzzy controller is applied to regulate the glucose concentration at a given set-point to increase flavone glycoside synthesis. The simulation results show that the proposed feeding strategy in a fed-batch culture enhances flavone glycoside production and the self-organizing fuzzy logic controller generated by genetic algorithm improves controller performance.     

High dose flaxseed oil supplementation may affect fasting blood serum glucose management in human type 2 diabetics

Type 2 diabetes is characterized partially by elevated fasting blood serum glucose and insulin concentrations and the percentage of hemoglobin as HbA1c. It was hypothesized that each of blood glucose and its co-factors insulin and HbA1c and would show a more favorable profile as the result of flaxseed oil supplementation. Patients were recruited at random from a population pool responding to a recruitment advertisement in the local newspaper and 2 area physicians. Completing the trial were 10 flaxseed oil males, 8 flaxseed oil females, 8 safflower (placebo) oil males and 6 safflower oil females. Patients visited on two pre-treatment occasions each three months apart (visits 1 and 2). At visit 2 subjects were randomly assigned in double blind fashion and in equal gender numbers to take flaxseed oil or safflower oil for three further months until visit 3. Oil consumption in both groups was approximately 10 g/d. ALA intake in the intervention group was approximately 5.5 g/d. Power was 0.80 to see a difference of 1 mmol of glucose /L using 12 subjects per group with a p < 0.05. Flaxseed oil had no impact on fasting blood serum glucose, insulin or HbA1c levels. It is concluded that high doses of flaxseed oil have no effect on glycemic control in type 2 diabetics.     

Identification and control of a riser‐type FCC unit

This paper addresses the use of feedforward neural networks for the steady‐state and dynamic identification and control of a riser type fluid catalytic cracking unit (FCCU). The results are compared with a conventional PI controller and a model predictive control (MPC) using a state space subspace identification algorithm. A back propagation algorithm with momentum term and adaptive learning rate is used for training the identification networks. The back propagation algorithm is also used for the neuro‐control of the process. It is shown that for a noise‐free system the adaptive neuro‐controller and the MPC are capable of maintaining the riser temperature, the pressure difference between the reactor vessel and the regenerator, and the catalyst bed level in the reactor vessel, in the presence of set‐point and disturbance changes. The MPC performs better than the neuro controller that in turn is superior to the conventional multi‐loop diagonal PI controller.     

Characteristics‐Based Model Predictive Control of a Catalytic Flow Reversal Reactor

This paper describes the formulation and tuning of a model‐based controller for a catalytic flow reversal reactor (CFRR). A plug flow non‐linear pseudo‐homogeneous mathematical representation of the process is used to model the mass and energy transport phenomena for the model‐based controller. A combination of the method of characteristics and model predictive control (MPC) technology is used to formulate the controller (Shang et al., Ind. Eng. Chem. Res. 43 (9) 2140–2149 (2004)). Mass extraction from the midsection of the reactor is used as the manipulated variable. Numerical simulations are used to show the performance of the formulated controller. The performance of the controller is evaluated on a simulated catalytic flow reversal reactor unit for combustion of lean methane streams for reduction of greenhouse gases emissions.