122 results found
Reddy M, Herrero P, Oliver N, et al., 2013, Clinical Evaluation of the Imperial College Bio-inspired Artificial Pancreas Overnight and After Breakfast in Adults with Type 1 Diabetes, European Association for the Study of Diabetes
Herrero P, Georgiou P, Oliver N, et al., 2013, A composite model of glucagon-glucose dynamics for in silico testing of bihormonal glucose controllers., J Diabetes Sci Technol, Vol: 7, Pages: 941-951
BACKGROUND: The utility of simulation environments in the development of an artificial pancreas for type 1 diabetes mellitus (T1DM) management is well established. The availability of a simulator that incorporates glucagon as a counterregulatory hormone to insulin would allow more efficient design of bihormonal glucose controllers. Existing models of the glucose regulatory system that incorporates glucagon action are difficult to identify without using tracer data. In this article, we present a novel model of glucagon-glucose dynamics that can be easily identified with standard clinical research data. METHODS: The minimal model of plasma glucose and insulin kinetics was extended to account for the action of glucagon on net endogenous glucose production by incorporating a new compartment. An existing subcutaneous insulin absorption model was used to account for subcutaneous insulin delivery. The same model of insulin pharmacokinetics was employed to model the pharmacokinetics of subcutaneous glucagon absorption. Finally, we incorporated an existing gastrointestinal absorption model to account for meal intake. Data from a closed-loop artificial pancreas study using a bihormonal controller on T1DM subjects were employed to identify the composite model. To test the validity of the proposed model, a bihormonal controller was designed using the identified model. RESULTS: Model parameters were identified with good precision, and an excellent fitting of the model with the experimental data was achieved. The proposed model allowed the design of a bihormonal controller and demonstrated its ability to improve glycemic control over a single-hormone controller. CONCLUSIONS: A novel composite model, which can be easily identified with standard clinical data, is able to account for the effect of exogenous insulin and glucagon infusion on glucose dynamics. This model represents another step toward the development of a bihormonal artificial pancreas.
González C, Herrero P, Cubero JM, et al., 2013, PREDIRCAM eHealth platform for individualized telemedical assistance for lifestyle modification in the treatment of obesity, diabetes, and cardiometabolic risk prevention: a pilot study (PREDIRCAM 1)., J Diabetes Sci Technol, Vol: 7, Pages: 888-897
BACKGROUND: Healthy diet and regular physical activity are powerful tools in reducing diabetes and cardiometabolic risk. Various international scientific and health organizations have advocated the use of new technologies to solve these problems. The PREDIRCAM project explores the contribution that a technological system could offer for the continuous monitoring of lifestyle habits and individualized treatment of obesity as well as cardiometabolic risk prevention. METHODS: PREDIRCAM is a technological platform for patients and professionals designed to improve the effectiveness of lifestyle behavior modifications through the intensive use of the latest information and communication technologies. The platform consists of a web-based application providing communication interface with monitoring devices of physiological variables, application for monitoring dietary intake, ad hoc electronic medical records, different communication channels, and an intelligent notification system. A 2-week feasibility study was conducted in 15 volunteers to assess the viability of the platform. RESULTS: The website received 244 visits (average time/session: 17 min 45 s). A total of 435 dietary intakes were recorded (average time for each intake registration, 4 min 42 s ± 2 min 30 s), 59 exercises were recorded in 20 heart rate monitor downloads, 43 topics were discussed through a forum, and 11 of the 15 volunteers expressed a favorable opinion toward the platform. Food intake recording was reported as the most laborious task. Ten of the volunteers considered long-term use of the platform to be feasible. CONCLUSIONS: The PREDIRCAM platform is technically ready for clinical evaluation. Training is required to use the platform and, in particular, for registration of dietary food intake.
Pagkalos I, Herrero P, Georgiou P, et al., 2013, An Analogue Implementation of the Beta-Cell Insulin Release Model, IEEE International Symposium on Circuits and Systems
This paper presents the implementation of a low-power analogue circuit, which replicates the granular release of insulin from beta-cell of the pancreas, to control the blood glucose. Results show that the circuit with a power consumption of 1.667 mW can achieve the same physiological responses with the designed model developed in Matlab. It is therefore expected that in a future implementation of the circuit in silicon, the same quality of blood glucose control can be achieved and therefore can be used as part of the Artificial Pancreas to support the treatment of Diabetes.
Pesl P, Herrero P, Reddy M, et al., 2013, Parameter Tuning of a Case-Based Reasoning Algorithm for Insulin Dosing Decision Support, Advanced Technologies & Treatments for Diabetes, 2013
Reddy M, Herrero P, Oliver N, et al., 2013, Clinical Assessment of the Imperial College Bio-inspired Artificial Pancreas (BiAP) in Subjects With Type 1 Diabetes Mellitus, Advanced Technologies & Treatments for Diabetes, 2013
Herrero P, Georgiou P, Oliver N, et al., 2013, In-Silico Comparison of a Bio-Inspired Glucose Controller vs. a PID controller with Insulin Feedback, Advanced Technologies & Treatments for Diabetes (ATTD)
A recently developed bio-inspired glucose controller (BIAP) based on a model of β-cell physiology is compared against a recently published PID controller with insulin feedback (PID-IF) using the UVa/Padova T1DM metabolic simulator.Both controllers were tuned using a 24-hour scenario containing a 40g(10am) carbohydrates meal and initial blood glucose of 160mg/dl. A single tuning parameter was adjusted to stabilize glucose around 100mg/dl, avoiding dropping below 80mg/dl. To compare controllers, a 24-hour scenario containing 3 meals (30g(6am), 40g(2pm), 20g(10pm)) was employed. In both controllers, a partial bolus corresponding to 50% of the required insulin to cover a meal was delivered at the time of ingestion. Standard metrics provided by the simulator were employed for comparison purposes.See table for results of BIAP and PID-IF in n=10 adult, n=10 adolescent and n=10 children in-silico subjects. Mean blood glucose with PID-IF was lower than with BIAP in the adult cohort, percentage of time below target with BIAP was lower in the children cohort, percentage of time within target with BIAP was higher in the adolescents cohort and risk index was lower with BIAP in the adult and adolescent groups.The tuning parameter in BIAP correlated very well with the insulin sensitivity factor of the test subjects (R2=0.9), while the correlation observed within PID-IF was lower (R2=0.6).The BIAP glucose controller performed comparably to the PID-IF controller in in-silico setting with superior metrics in an adolescent cohort. The BIAP controller can be easily tuned by using a simple correlation with the insulin sensitivity factor.
Herrero P, Yoong WK, Georgiou P, et al., 2012, A Bio-Inspired Insulin Bolus, Diabetes Technology Meeting
Objective:In-vitro physiological data of insulin release in beta cells reveals that glucose-induced insulin secretion is comprised of a transient insulin release (first phase) followed by a gradually developing secondary stimulation (second phase).This work aims to develop a novel insulin bolus wave inspired by the observed biphasic insulin release to provide more efficient glycemic control for subjects using bolus waves in current subcutaneous insulin infusion pumps (single wave, dual wave and square wave).Method:The bio-inspired insulin bolus (BIB) is composed of an initial single wave (B) at time T0 followed by a trapezoidal shape defined by two insulin infusion rates (R1 and R2) and the corresponding times (T1 and T2). BIB parameters are optimized for a mixed meal model library using a composite type 1 diabetes (T1DM) minimal model of glucose-insulin dynamics and a least squares grid search parameter identification technique. The blood glucose risk index (BGRI) was used to assess the performance of the evaluated bolus waves.Result: In-silico testing shows that BIB provides better, or equivalent in the worst case, glycemic control (15% average relative BGRI reduction), with respect to other existing bolus waves, over 23 tested mixed meals. BIB showed greater glycemic control improvements in slow absorption meals than fast absorption meals.Conclusion:An insulin bolus wave inspired by the physiology of insulin release from the beta cells may help to improve glycemic control in T1DM subjects. Prior individualization of the bio-inspired bolus wave parameters is required for optimal control of different types of mixed meals.
Herrero P, Georgiou P, Delaunay B, et al., 2012, Robust Parameter Estimation of Glucose-Insulin Minimal Models using Interval Analysis, Diabetes Technology Meeting
Objective:Minimal models of glucose-insulin metabolism are currently being used in several diabetes-related applications, such as assessment of the insulin sensitivity and glucose effectiveness, first and second phase pancreatic response, model-based glucose controllers and model-based fault detection techniques. Robust evaluation of these methods has created a need for bounded error parameter estimation techniques. This work presents an innovative method for guaranteed non-linear parameter estimation of metabolic minimal models based on interval analysis.Method:An efficient vectorial implementation in Matlab of Set Inversion Via Interval Analysis Algorithm (SIVIA) was developed for this purpose. In order to reduce numerical overestimation associated with interval arithmetic, Modal Interval Analysis was employed. Clinical data from standard oral glucose tolerance test (OGTT) and meal tolerance test (MTT), as well as in-silico data, were used to prove the validity of the proposed approach.Result: Despite the exponential complexity of the SIVIA algorithm due to its branch-and-bound nature, the proposed implementation, with a tolerance of 1% (stopping criteria), was able to identify the 4 parameters of the glucose-insulin minimal model (p1, p2, p3, g0), for an OGTT data set with 2% error in plasma glucose measurements and 3% errors in plasma insulin measurements, in a computation time of 290 seconds (Intel Dual Core 3.16GHz). Conclusion:Set inversion via interval analysis is a suitable tool for robust parameter estimation of the glucose-insulin minimal model.
Herrero P, Delaunay B, Jaulin L, et al., 2012, An Efficient Implementation of the SIVIA Algorithm in a High-Level Numerical Programming Language, Reliable Computing, Pages: 239-251
High-level, numerically oriented programming languages such as Mat-lab, Scilab or Octave are popular and well-established tools in the sci-enti c and engineering communities. However, their computational e -ciency sometimes limits their use in certain areas where intensive numer-ical computations are required, such as interval analysis. In this paper,we present an e cient implementation of the well known Set Inverter viaInterval Analysis (SIVIA) algorithm in Matlab that has a computationale ciency comparable to its C++ counterpart. Such implementation aimsat promoting and facilitating the use of SIVIA algorithm by the afore-mentioned communities. The source code of a Matlab implementation isfreely distributed.
Herrero P, Calm R, Vehi J, et al., 2012, Robust Fault Detection System For Insulin Pump Therapy using Continuous Glucose Monitoring, Journal of Diabetes Science and Technology, Vol: 6, Pages: 1131-1141
Background:The popularity of continuous subcutaneous insulin infusion (CSII), or insulin pump therapy, as a way to deliver insulin more physiologically and achieve better glycemic control in diabetes patients has increased. Despite the substantiated therapeutic advantages of using CSII, its use has also been associated with an increased risk of technical malfunctioning of the device, which leads to an increased risk of acute metabolic complications, such as diabetic ketoacidosis. Current insulin pumps already incorporate systems to detect some types of faults, such as obstructions in the infusion set, but are not able to detect other types of fault such as the disconnection or leakage of the infusion set. Methods:In this article, we propose utilizing a validated robust model-based fault detection technique, based on interval analysis, for detecting disconnections of the insulin infusion set. For this purpose, a previously validated metabolic model of glucose regulation in type 1 diabetes mellitus (T1DM) and a continuous glucose monitoring device were used. As a first step to assess the performance of the presented fault detection system, a Food and Drug Administration-accepted T1DM simulator was employed.Results:Of the 100 in silico tests (10 scenarios on 10 subjects), only two false negatives and one false positive occurred. All faults were detected before plasma glucose concentration reached 300 mg/dl, with a mean plasma glucose detection value of 163 mg/dl and a mean detection time of 200 min.Conclusions:Interval model-based fault detection has been proven (in silico) to be an effective tool for detecting disconnection faults in sensor-augmented CSII systems. Proper quantification of the uncertainty associated with the employed model has been observed to be crucial for the good performance of the proposed approach.
Herrero P, Georgiou P, Oliver N, et al., 2012, Simulation For Bihormonal Glucose ControllerTesting, Incorporating Intra-Day Variability And A Meal Library., Advanced Technologies & Treatments for Diabetes
Objective: Subcutaneous administration of glucagon as a counterregulatory action to subcutaneous insulin infusion is rapidly gaining attention in the community. However, currently available simulators for designing and testing glucose controllers do not contemplate glucagon as control input. Furthermore, these environments do not usually incorporate intra-day variability on metabolic parameters and meal composition. In this work, we present a more realistic type 1 diabetic simulator that includes, in addition to insulin infusion, glucagon infusion. Furthermore, our simulator incorporates intra-day variability and a meal library that allows representation of variability in meal composition.Method: A glucagon-extended model was identified using data from a bihormonal closed-loop artificial pancreas trial. Intra-day variability of model parameters was incorporated during the identification process by allowing parameters to vary through the day. To build the meal library, a novel model-based technique for identifying the rate of glucose appearance was employed. For this purpose, data from the scientific literature, including plasma glucose and plasma insulin concentration, were used.Result: The novel simulation environment has been implemented using MatlabTM and is intended to be freely distributed under GLP license. The simulator has already been used for testing a bio-inspired glucose controller for insulin delivery combined with a PD controller for glucagon delivery. Initial results show that tighter glycaemic control can be achieved by using a bihormonal approach. Conclusion: This unique simulation environment enables, for the first time, the testing of bihormonal glucose controllers in more realistic scenarios. This achievement represents another step towards a fully physiological artificial pancreas.
Herrero P, Bondia J, Palerm CC, et al., 2012, A simple robust method for estimating the glucose rate of appearance from mixed meals., J Diabetes Sci Technol, Vol: 6, Pages: 153-162
BACKGROUND: Estimating the rate of glucose appearance (R(a)) after ingestion of a mixed meal may be highly valuable in diabetes management. The gold standard technique for estimating R(a) is the use of a multitracer oral glucose protocol. However, this technique is complex and is usually not convenient for large studies. Alternatively, a simpler approach based on the glucose-insulin minimal model is available. The main drawback of this last approach is that it also requires a gastrointestinal model, something that may lead to identifiability problems. METHODS: In this article, we present an alternative, easy-to-use method based on the glucose-insulin minimal model for estimation of R(a). This new technique avoids complex experimental protocols by only requiring data from a standard meal tolerance test. Unlike other model-based approaches, this new approach does not require a gastrointestinal model, which leads to a much simpler solution. Furthermore, this novel technique requires the identification of only one parameter of the minimal model because the rest of the model parameters are considered to have small variability. In order to account for such variability as well as to account for errors associated to measurements, interval analysis has been employed. RESULTS: The current technique has been validated using data from a United States Food and Drug Administration-accepted type 1 diabetes simulator [root mean square error (RMSE) = 0.77] and successfully tested with two clinical data sets from the literature (RMSE = 0.69). CONCLUSIONS: The presented technique for the estimation of R(a) showed excellent results when tested with simulated and actual clinical data. The simplicity of this new technique makes it suitable for large clinical research studies for the evaluation of the role of R(a) in patients with impairments in glucose metabolism. In addition, this technique is being used to build a model library of mixed meals that could be incorporated into diabetic su
Herrero P, Georgiou P, Oliver N, et al., 2012, A Bio-Inspired Glucose Controller Based on the Pancreatic β-Cell Physiology, Journal of Diabetes Science and Technology, Vol: 6
Pagkalos I, Herrero P, El-Sharkawy M, et al., 2011, VHDL implementation of the Biostator II glucose control algorithm for critical care., Biomedical Circuits and Systems Conference, Pages: 94-97
This paper presents the hardware implementation and optimisation of the Biostator II algorithm for control of blood glucose in the hospital. Elevated blood glucose levels occur frequently in hospitalised patients suffering from acute illness and needs to be controlled to minimise secondary complications. Unlike previous efforts, we use subcutaneous glucose sensing, minimising risks and discomfort associated with the intravenous route. Implemented in VHDL, the algorithm is geared towards integration in a CMOS ASIC, combining sensor instrumentation as well as serving as the core for a portable, reliable and affordable system for safe and effective control of blood glucose in hospitalised patient using commercially available subcutaneous continuous glucose sensors and intravenous insulin pumps. The algorithm has been successfully tested in hardware using a commercially available simulator of type 1 diabetes mellitus subjects. It achieves good control with the adults subjects being 98.7% in target with a mean blood glucose of 134.67 mg/dl and the adolescent subjects being 93.56% in target with a mean blood glucose of 126.01 mg/dl.
Herrero P, Georgiou P, Oliver N, et al., 2011, An Insulin Bolus Calculator based on Case-Based Reasoning, Diabetes Technology Society
Objective:Nowadays most of the commercially available insulin pumps come with a new feature called bolus calculator. Recent studies have shown the clinical benefit of using such insulin bolus recommenders, but their performance is still far from being optimal and their utilization is not extended over the diabetic population. In order to achieve a significant improvement on glycemic control, these systems require an intensive initial tuning and a continuous follow-up by an expert clinician. In this work, we present a new insulin bolus recommender based on Case-Based Reasoning (CBR), a consolidated artificial intelligence technique, which solves newly encountered problems by applying the solutions learned from solving previous problems. CBR is known to provide more flexibility and adaptability than the rule-based approach used by current bolus calculators, something that may be very useful in an extremely uncertain and variable problem such as diabetes management.Method: A proprietary algorithm incorporating the full CBR cycle (i.e. retrieve, reuse, revise and retain) was implemented. The algorithm can be initialized with the subject’s standard bolus therapy or with a patient specific case memory obtained from retrospective clinical data. The CBR algorithm was in-silico evaluated using the commercial version of the FDA-accepted T1DM simulator (University of Virginia, VA). For this purpose, the default bolus therapy from the simulator was compared with the proposed algorithm. A one-month scenario, with realistic variability on the meal ingestion times and carbohydrate amounts, was used for this purpose.Result: With the new proposed algorithm, a reduction of 20% on mean blood glucose concentration and an increase of 3% of time in the target range [70, 180] mg/dL, without a significant increase of hypoglycemic events, were achieved.Conclusion:Case-Based Reasoning has been proven to be a viable technology for providing insulin bolus recommendations, potentially req
Herrero P, Georgiou P, Oliver N, et al., 2011, In-Silico Validation of a Bio-Inspired Glucose Controller, Diabetes Technology Society
Objective:Bio-inspired approaches for solving medical problems have been motivated by the belief that nature has evolved over millions of years to carry out its tasks more optimally and efficiently. Therefore, replicating the functionality of the human body can lead to a system with greater physiological function, which the body understands and therefore may be able to deliver a healthier therapy.In this work, a recently developed glucose controller based on a mathematical model of the β-cell physiology of the pancreas is validated using the 100 adult and 100 adolescent type 1 diabetes mellitus virtual populations accepted by the US Federal Drug Administration (FDA).Method: Two versions of the controller where tested, both with meal announcement, but one with pre-meal bolus and the other without pre-meal bolus. However, the pre-meal bolus version was tested using a non-FDA-accepted population. The in-silico tests were carried out by the Epsilon Group (University of Virginia, VA). An already published meal protocol was used for this purposeResult: Results are expressed as mean±SD blood glucose (BG) levels (mg/dL) and in percentage of time (%). The glucose target was set to [70, 180] mg/dL.Version Population Mean BG % in target % < 50 % < 70 % > 180 % > 300Without Adults 125±12 92.8 ± 7.3 0±0 0.44±2.2 6.7±6.9 0.09±0.5bolus Adolescents 133±17 83.5 ± 14 0.09±0.7 1.7±4.9 14.7±11.9 0.8±2.3With Adults 118±9 97.8±4 0±0 0.22±1.3 1.9±3.3 0±0bolus Adolescents 133±13 84.9±12.3 0±0 1.2±3.7 13.8±11.2 0.3±1.3Conclusion:Both versions of the controller showed very good glycemic control over the two virtual populations. Despite being the FDA-accepted and non-FDA-accepted populations statistically very similar, the results are not totally comparable. Nevertheless, the pre-meal bolus version was
Herrero P, Georgiou P, Oliver N, et al., 2011, A Glucagon-Extended Minimal Model for In-Silico Testing of Glucose Controllers, Advanced Technologies & Treatments for Diabetes
Objective:In-silico validation of glucose controllers has been proven to be a valuable tool to progress towards an artificial pancreas. Nevertheless, current available simulators only incorporate insulin, as a regulatory hormone, and omit the rest of counterrregulatory hormones (i.e. glucagon). In this work, the classic glucose-insulin minimal model is extended in order to incorporate the glucagon effect on the glucose regulation. This new model opens the door to the testing of bi-hormonal glucose controllers. Method: In order to deal with subcutaneous (s.c.) insulin infusion and carbohydrate ingestion, existing models of s.c. insulin absorption and a gastro-intestinal absorption were incorporated to the original glucose-insulin minimal model. The same s.c. insulin absorption model was adapted for modeling the s.c. glucagon absorption. A model of the currently available continuous glucose sensors was employed to represent the glucose measurements errors. Finally, an additional compartment was added to the minimal model in order to incorporate the effect of glucagon on the glucose regulation. Clinical data from closed-loop trials of a bi-hormonal controller were employed to identify the parameters of the model.Result: The current glucagon-extended minimal model has already been used for testing a novel bio-inspired glucose controller. The obtained results show that tighter glycemic control can be achieved by using a bi-hormonal approach. Conclusion:A novel extended minimal model incorporating glucagon is presented and validated. The presented model allows the testing of bi-hormonal glucose controllers in the context of an artificial pancreas. A cohort of virtual type 1 diabetic subjects is currently being implemented in order to incorporate the inter-patient variability.
Herrero P, Bondia J, Palerm C, et al., 2010, A Simple Method for Estimating the Rate of Glucose Appearance from Mixed Meals, Diabetes Technology Society
Objective:Estimating the rate of glucose appearance (Ra) into the peripheral circulation, after the ingestion of a mixed meal, may be highly valuable in diabetes management. In the artificial pancreas context, it is of great importance to estimate the contribution of Ra into the overall glucose kinetics, since an accurate prediction of plasma glucose is crucial for most glucose controllers.Current existing techniques for estimating Ra are either experimentally complex (i.e. multi-tracer protocols) or numerically complex (i.e. Bayesian estimation). In this work, a novel and simple method is proposed.Method: Unlike other existing methodologies based on the glucose-insulin minimal model, the presented one does not require from a gastrointestinal absorption model. This difference leads to a much easier problem solving, since it avoids the identifiability problems that usually arise when such a model is considered. As for estimating Ra an estimate of the insulin sensitivity (SI) is required, a new technique for estimating SI is also presented.In order to validate the proposed technique, a FDA approved type 1 diabetic simulator has been employed. Furthermore, the new technique has been tested with two sets of clinical data showing promising results.Result: For the 10 selected adult virtual subjects, the average coefficient of determination between the estimated individual Ra and the ”real” one obtained from the simulator was R2 = 0.94.Furthermore, the proposed technique for estimating SI correlated very well with an existing validated method (R2 = 0.99). For the two sets of clinical data, the correlation was R2 = 0.92 and R2 = 0.65.Conclusion:A novel and simple method for estimating Ra is presented and validated. The proposed technique is currently being applied for building a library of mixed meals using data from the scientific literature. This library could be incorporated into the current simulators in order to account with more realistic meals.
Herrero P, Pantelis G, Oliver N, et al., 2010, A Novel Bio-Inspired Glucose Controller, Diabetes Technology Society, Publisher: Diabetes Technology Society
Objective:To date, control algorithms used in the context of an artificial pancreas (AP) have been mainly based on classical control engineering techniques such as proportional-integral-derivative control and model-predictive control. Developments of mathematical models of β-cell physiology, which are able to describe the glucose-induced insulin release at a molecular level, have opened the door to a new class of promising bio-inspired glucose control algorithms. In this work, a novel bio-inspired glucose controller is presented.Method: A recently developed subcellular model of glucose-stimulated pancreatic insulin secretion is used as the core of the proposed controller. As the subcutaneous route is employed for both glucose sensing and insulin delivery, the new controller incorporates an insulin feedback term in order to deal with time delays and avoid insulin overdosing. A type 1 diabetic subject simulator (University of Virginia) was used to validate the controller in silico.Result: For the 10 adult subjects of the simulator, using a standard meal protocol with meal announcement, the following results were obtained: mean blood glucose = 120.9 mg/dl, percentage below target = 0, percentage above target = 6.5, and percentage within target = 92, where glucose target was [70, 180] mg/dl.For the 10 adolescent subjects, mean blood glucose = 135.2 mg/dl, percentage below target = 0, percentage above target = 17.3, and percentage within target = 82.7.Conclusion:A novel bio-inspired glucose controller is presented and validated in silico. The proposed technique is currently being implemented in hardware and integrated in an AP platform. The controller is expected to be clinically tested in the following year.
Bondia J, Vehi J, Palerm CC, et al., 2010, Artificial pancreas: automatic control of insulin infusion in type 1 diabetes mellitus, REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL, Vol: 7, Pages: 5-+, ISSN: 1697-7912
Bondia J, Vehí J, Palerm CC, et al., 2010, El Páncreas Artificial: Control Automático de Infusión de Insulina en Diabetes Mellitus Tipo 1, Revista Iberoamericana de Automática e Informática Industrial RIAI, Vol: 7, Pages: 5-20, ISSN: 1697-7912
Herrero P, Jaullin L, Vehi J, et al., 2010, Guaranteed Set-point Computation with Application to the Control of a Sailboat, INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, Vol: 8, Pages: 1-7, ISSN: 1598-6446
Armengol J, Vehi J, Angel Sainz M, et al., 2009, SQualTrack: A Tool for Robust Fault Detection, IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, Vol: 39, Pages: 475-488, ISSN: 1083-4419
Wan J, Vehi J, Luo N, et al., 2009, CONTROL OF CONSTRAINED NONLINEAR UNCERTAIN DISCRETE-TIME SYSTEMS VIA ROBUST CONTROLLABLE SETS: A MODAL INTERVAL ANALYSIS APPROACH, ESAIM-CONTROL OPTIMISATION AND CALCULUS OF VARIATIONS, Vol: 15, Pages: 189-204, ISSN: 1292-8119
Sainz MA, Herrero P, Armengol J, et al., 2008, Continuous minimax optimization using modal intervals, JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, Vol: 339, Pages: 18-30, ISSN: 0022-247X
Herrero P, Sainz MA, Veh J, et al., 2005, Quantified Set Inversion Algorithm with Applications to Control, Reliable Computing, Vol: 11, Pages: 369-382, ISSN: 1385-3139
Armengol J, Vehi J, Sainz MA, et al., 2004, Application of interval models to the detection of faults in industrial processes, 5th International Symposium on Soft Computing for Industry held at the 6th Biannual World Automation Congress, Publisher: TSI PRESS, Pages: 269-274
Armengol J, Vehí J, Sainz MÁ, et al., 2003, Fault Detection in a Pilot Plant Using Interval Models and Multiple Sliding Time Windows, IFAC Proceedings Volumes, Vol: 36, Pages: 681-686, ISSN: 1474-6670
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