Enhanced Model Predictive Control Research Articles

Enhanced Model Predictive Control for Induction Motor Drives in Marine Electric Power Propulsion System

Marine electric propulsion is an important topic in the research of modern ships and underwater vehicles. The propulsion motor drives based on model predictive control (MPC) are becoming increasingly popular in marine propulsion systems as an emerging technology. However, the multi-objective optimization in conventional MPC requires cumbersome weighting factor tuning. The relatively large computational cost is also detrimental to the industrial application of MPC. Aiming at reducing the computational complexity of multi-objective optimization without weighting factors, this paper proposes an enhanced ranking-based MPC method for induction motor drives in marine electric power propulsion. The presented control set pre-optimization aims to reduce the computational complexity of enumeration and ranking. Based on the sign of torque prediction deviation, the proposed method avoids enumerating all fundamental voltage vectors. Consequently, the number of candidate elements in the initial control set are reduced to four without excessively excluding feasible solutions. By converting predicted numerical errors into ranking results, the proposed MPC seeks the optimal solution among the candidates through improved ranking evaluation. Considering the situation of simultaneous optimal ranking, the normalization error judgment is developed to further optimize the optimal solution selection process. The simulation and experimental results confirm that the proposed MPC is simple and effective. Without the involvement of tuning the weighting factors, the proposed method achieves good performance.

Enhanced Model Predictive Control for PMSM Based on Reference Voltage Predictive Model

Enhanced Model Predictive Control for PMSM Based on Reference Voltage Predictive Model

Energy-efficient hybrid model predictive control of mobile refrigeration systems

Efficiency and temperature compliance of last-mile refrigerated transport suffer from more severe disturbances than stationary applications. The significant energy expenditure and diminished quality or loss of goods have been tackled recently by improved hardware design. However, standard operating strategies only partly use accompanying intrinsic potential. This article introduces an enhanced model predictive control scheme for mobile refrigeration systems with secondary loop cooling units, aiming toward energy-efficient operation complying with temperature restrictions. Its real-time capable optimization covers operational flexibilities and dynamics (storage capacities, door openings, permissible temperature range) more extensively than state-of-the-art solutions. Experimental validation on a specifically designed test rig uses a representative test run with expected and unexpected door openings and a pull-down sequence. A proportional–integral and a standard predictive controller serve for comparison. Performance assessment shows energy savings of 16.4% and improvement in temperature compliance of 3.4% of the advanced predictive over the proportional–integral scheme. If door openings are known upfront, energy consumption decreases by even 29.6%. The standard predictive algorithm shows a similar energy performance but substantially declined temperature compliance as it cannot harness the entire flexibility. Thus, the proposed advanced strategy significantly contributes to more efficient refrigerated transport with reduced environmental impact and loss of goods.

EKF enhanced MPC for rapid attitude stabilization of space robots with bounded control torque in postcapture

Space robots play a more and more important role in on-orbit service missions. It is essential for a space robot to stabilize its attitude rapidly after capturing an unknown target, because of its residual momentum. Regarding uncertainties in inertial parameters of the target, process and measurement, and insufficient measurements, this paper first presents extended Kalman filter (EKF) enhanced model predictive control (MPC) strategy for rapid attitude stabilization of space robots with bounded control torque of the actuator and the manipulator in postcapture, in which motion of the manipulator is planned to make up for the limitation of the actuator. Thanks to the Jacobian matrix, which is required for EKF, the linear varying time-MPC is applied to handle the constraints of the drive torque, which can be solved by the online active set method effectively and efficiently. Simulation has been done for the 2D and the 3D models, and the results show that the whole system, including the base and the manipulator can be stabilized, and inertial parameters of the target can converge to their ideal values with the constraints. In addition, robustness of the presented method has been verified by Monte Carlo simulation.

Model Predictive Control Based Energy Management System Literature Assessment for RES Integration

Over the past few decades, the electric power industry evolved in response to growing concerns about climate change and the rising price of fossil fuels. The usage of renewable energy sources (RES) rose as a remedy for these problems. The increased penetration of RES in the existing generation system increased the need for an intelligent energy management system (EMS) so that the system can operate in any possible circumstances. Many sectors of society, including the education sector, are working to realize the importance of this sustainable energy system. This paper reviews the process of selecting an efficient control technique for continuous power flow from different RES to meet the load demand requirement using an enhanced model predictive control (MPC)-based EMS framework. This EMS is a software platform to provide fundamental support services and applications to deliver the functionality needed for the effective operation of electrical generation and transmission facilities to ensure adequate security of energy supply at minimum cost. The centralized EMS with technical objectives focusing on power quality and seamless power flow can be achieved through dynamically enhanced MPC.

Enhanced MPC based on unknown state estimation and control compensation

The model predictive control (MPC) method is widely used in multivariable process control due to its optimization control nature and easy engineering realization. Aiming at the large control error fluctuations caused by various noises, unmeasurable states, and disturbances under the MPC method in practice, this paper proposes a novel enhanced MPC (En-MPC) method that uses Kalman filter to estimate unknown states and combines with the state gain matrix for control compensation. Firstly, given the difficulty of measuring some key states of actual industrial processes, the unknown states are estimated online through Kalman filter technology; Secondly, the state estimation values are used as the initial value of the prediction model to obtain the future output information of the system, and the open-loop optimization solution is calculated in the finite horizon by solving the optimization objective function. The relationship equation between the state variance and the state gain matrix is established and optimized to obtain the optimal gain matrix, which is multiplied by the state estimation as the output of the compensation controller. Finally, the solution of the basic optimization controller and the solution of the compensator is added together to act as the final control input to the controlled plant. The upper bounds of the state variables of the proposed method are proved by the induction method in the root-mean-square sense, and the stability of the system under the algorithm is demonstrated. Simulations and sewage treatment process data experiment show the effectiveness and practicability of the proposed method.

An Optimal Control Strategy for Plug-In Hybrid Electric Vehicles Based on Enhanced Model Predictive Control With Efficient Numerical Method

Advances in machine learning inspire novel solutions for the validation of complex vehicle models and spur an easy manner to promote energy management performance of complexly configured vehicles, such as plug-in hybrid electric vehicles (PHEVs). A constructed PHEV model, based on the four-wheel-drive passenger vehicle configuration, is validated through an efficient virtual test controller (VTC) developed in this article. The VTC is designed via a novel approach based on the least-squares support vector machine and random forest with the inner-interim data filtered by the ReliefF algorithm to validate the vehicle model as necessary. This article discusses the process and highlights the accuracy improvements of the PHEV model that is achieved by implementing the VTC. The validity of the VTC is addressed by examining the PHEV model to mimic the characteristics of the internal combustion engine, motor, and generator behaviors observed through the benchmark test. Sufficient simulations and hardware-in-the-loop tests are employed to demonstrate the capability of the novel VTC-based model validation method in practical applications. The major novelty of this article lies in the development of a VTC, by which the vehicle model can be efficiently developed, providing a solid framework and enormous convenience for control strategy design.

Effectiveness and safety of the Space GlucoseControl system for glycaemia control in caring for postoperative cardiac surgical patients

BackgroundHyperglycaemia is a very common complication in post–cardiac surgical patients, and as such, it must be properly managed. For this purpose, the enhanced Model Predictive Control algorithm for glycaemia control has been implemented into a nurse-led device called Space GlucoseControl (SGC) that aims to achieve a safe and effective blood glucose control in a better way than the traditional “paper-based” protocols. PurposeThe aim of the study was to know the effectiveness and safety of the SGC in glycaemia control in cardiosurgical adult patients in the immediate postoperative period in the intensive care unit. MethodsA prospective before-and-after intervention study was conducted. One hundred sixty cardiosurgical adult patients with hyperglycaemia were selected: 80 in the control group from May to November 2018 and 80 in the intervention group (use of the SGC device) from January to December 2019. The primary outcome was the percentage of time within the target range (140–180 mg/dL in the control group and 100–160 mg/dL in the intervention group). ResultsThe percentage of time within the target range was significantly higher in the SGC group than in the control group (70.5% [58.25–80] vs 54.83% [36.09–75], p < 0.001). The range was also achieved earlier with the SGC (5 [3–6.875] hours vs 7 [4–11] hours; p < 0.05). The first blood glucose value after reaching the target range was higher in the control group, with statistical significance (p < 0.05). There were no hypoglycaemia episodes in the control group. However, during SGC treatment, six episodes of hypoglycaemia occurred, and all of them were nonsevere (mean value = 61 mg/dL). ConclusionThe SGC is useful to achieve a faster tight glycaemic control, with a higher percentage of time within the target range, although episodes of nonsevere hypoglycaemia could be observed.

Evaluation of an Artificial Pancreas with Enhanced Model Predictive Control and a Glucose Prediction Trust Index with Unannounced Exercise.

We investigated the safety and efficacy of the addition of a trust index to enhanced Model Predictive Control (eMPC) Artificial Pancreas (AP) that works by adjusting the responsiveness of the controller's insulin delivery based on the confidence intervals around predictions of glucose trends. This constitutes a dynamic adaptation of the controller's parameters in contrast with the widespread AP implementation of individualized fixed controller tuning. After 1 week of sensor-augmented pump (SAP) use, subjects completed a 48-h AP admission that included three meals/day (carbohydrate range 29-57 g/meal), a 1-h unannounced brisk walk, and two overnight periods. Endpoints included sensor glucose percentage time 70-180, <70, >180 mg/dL, number of hypoglycemic events, and assessment of the trust index versus standard eMPC glucose predictions. Baseline characteristics for the 15 subjects who completed the study (mean ± SD) were age 46.1 ± 17.8 years, HbA1c 7.2% ± 1.0%, diabetes duration 26.8 ± 17.6 years, and total daily dose (TDD) 35.5 ± 16.4 U/day. Mean sensor glucose percent time 70-180 mg/dL (88.0% ± 8.0% vs. 74.6% ± 9.4%), <70 mg/dL (1.5% ± 1.9% vs. 7.8% ± 6.0%), and number of hypoglycemic events (0.6 ± 0.6 vs. 6.3 ± 3.4), all showed statistically significant improvement during AP use compared with the SAP run-in (P < 0.001). On average, the trust index enhanced controller responsiveness to predicted hyper- and hypoglycemia by 26% (P < 0.005). In this population of well-controlled patients, we conclude that eMPC with trust index AP achieved nearly 90% time in the target glucose range. Additional studies will further validate these results.

Artificial Pancreas with Glucose Prediction Trust Index Improves Time-in-Target Glucose Range vs. Sensor-Augmented Pump Therapy

Objective: To investigate the safety and efficacy of the addition of a trust index to enhanced Model Predictive Control (eMPC) Artificial Pancreas (AP) that works by adjusting the aggressiveness of the controller’s insulin delivery based on the confidence intervals around predictions of glucose trends. Methods: After one week of sensor-augmented pump (SAP) use, subjects completed a 48-hour AP admission that included 3 meals/day of at least 30 g CHO per meal, 1 hour of unannounced exercise and two overnight periods. Endpoints included sensor glucose percentage time 70-180 mg/dL, &amp;lt;70 mg/dL, &amp;gt;180 mg/dL, number of hypoglycemic events, and assessment of the trust index vs. standard eMPC glucose predictions. Results: Baseline characteristics for the 15 subjects (mean±SD) were age 46.1±17.8 years, HbA1c 7.2±1.0%, diabetes duration 26.8±17.6 years and TDD 35.5±16.4 units/day. Glycemic outcomes are reported in the table. Mean percent time 70-180 mg/dL (88.0±7.7%), &amp;lt;70 mg/dL (1.5±1.8%), and number of hypoglycemic events (0.6±0.6%) all showed statistically significant improvement during AP (p&amp;lt;0.001). On average, the trust index enhanced controller responsiveness to predicted hyper- and hypoglycemia by 26% (p&amp;lt;0.005). Glycemic Outcomes. eMPC Artificial Pancreas with Trust Index vs. Sensor-Augmented Pump.Glycemic Outcomes (Mean±SD)48-Hour AP SessionSAP Run-In Weekp-valuePercentage Time 70-180, mg/dL88.0±7.774.6±9.0&amp;lt;0.001Percentage Time 80-140, mg/dL60.1±13.445.2±10.30.004Percentage Time &amp;lt; 70, mg/dL1.5±1.87.8±5.8&amp;lt;0.001Percentage Time &amp;gt; 180, mg/dL10.5±7.717.6±10.40.081Mean CGM, mg/dL130.1±14.7135.1±18.8&amp;gt;0.5Standard Deviation, mg/dL32.9±6.150.3±10.5&amp;lt;0.001Number of daily hypoglycemic events (CGM &amp;lt;70 mg/dL for ≥ 15 minutes)0.6±0.61.7±0.9&amp;lt;0.001 Conclusions: eMPC AP with trust index additions achieved nearly 90% time in the target glucose range. Additional studies will further validate these results. Disclosure J.E. Pinsker: Research Support; Self; Insulet Corporation, Dexcom, Inc., Tandem Diabetes Care, Inc.. A.J. Laguna Sanz: None. M. Church: None. J. Lee: Employee; Self; Insulet Corporation. L. Lindsey: None. C.C. Andre: None. F.J. Doyle: Research Support; Self; Insulet Corporation, Roche Diagnostics Corporation, Xeris Pharmaceuticals, Inc., Dexcom, Inc., DreaMed Diabetes, Ltd., LifeScan, Inc.. Other Relationship; Self; ModeAGC. E. Dassau: Consultant; Self; Insulet Corporation. Research Support; Self; Insulet Corporation. Consultant; Self; Animas Corporation. Research Support; Self; Dexcom, Inc.. Speaker's Bureau; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Roche Diabetes Care Health and Digital Solutions, Xeris Pharmaceuticals, Inc.. Consultant; Self; Eli Lilly and Company. Research Support; Self; Tandem Diabetes Care, Inc.. Other Relationship; Self; ModAGC. Research Support; Self; LifeScan, Inc., DreaMed Diabetes, Ltd..

Comparison of Space Glucose Control and Routine Glucose Management Protocol for Glycemic Control in Critically Ill Patients: A Prospective, Randomized Clinical Study.

Background:The Space Glucose Control (SGC) system is a computer-assisted device combining infusion pumps with the enhanced Model Predictive Control algorithm to achieve the target blood glucose (BG) level safely. The objective of this study was to evaluate the efficacy and safety of glycemic control by SGC with customized BG target range of 5.8–8.9 mmol/L in the critically ill patients.Methods:It is a randomized controlled trial of seventy critically ill patients with mechanical ventilation and hyperglycemia (BG ≥ 9.0 mmol/L). Thirty-six patients in the SGC group and 34 in the routine glucose management group were observed for three consecutive days. Target BG for both groups was 5.8–8.9 mmol/L. The primary outcome was the percentage time in the target range.Results:The percentage time within BG target range in the SGC group (69 ± 15%) was significantly higher than in the routine management group (52 ± 24%; P < 0.01). No measurement was ≤2.2 mmol/L, and there was only one episode of hypoglycemia (2.3–3.3 mmol/L) in each group. The average BG was significantly lower in the SGC group (7.8 ± 0.7 mmol/L) than in the routine management group (9.1 ± 1.6 mmol/L, P < 0.001). Target BG level was reached earlier in the SGC group than routine management group (2.5 ± 2.9 vs. 12.1 ± 15.3 h, P = 0.001). However, the SGC group performed worse for daily insulin requirement (59.8 ± 39.3 vs. 28.4 ± 36.7 U, P = 0.001) and sampling interval (2.0 ± 0.5 vs. 3.7 ± 0.5 h, P < 0.001) than the routine management group did. Multiple linear regression showed that the intervention group remained a significant individual predictor (P < 0.001) of the percentage time in target range.Conclusions:The SGC system, with a BG target of 5.8–8.9 mmol/L, resulted in effective and reliable glycemic control with few hypoglycemic episodes in critically ill patients with mechanical ventilation and hyperglycemia. However, the workload was increased.Trial Registration:http://www.clinicaltrials.gov, NCT 02491346; https://www.clinicaltrials.gov/ct2/show/NCT02491346?term=NCT02491346&cond=Hyperglycemia&cntry1=ES%3ACN&rank=1.

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions.

Model predictive control (MPC) performance depends on the accuracy of the prediction model implemented by the controller. Complex physiology and modeling limitations often prevent the ability to provide long and accurate glucose predictions, which results in the need to account for prediction errors. Optimal insulin dosage by Zone-MPC is calculated by solving an optimization problem in which a scalar index is minimized by penalizing relative input deviations and glucose predictions out of the reference zone. The controller's tuning parameters are the penalties on the input variable (insulin). Positive and negative relative inputs are penalized differently. A dynamic adaptation of the tuning parameters based on the accuracy of the model in recent history is implemented in this article and compared in silico to aggressive and conservative tunings of the same controller structure. Similar average glucose and time in the safe glucose range (70-180 mg/dL) are achieved for the adaptive design and traditional controller configurations. However, percentage time under 70 mg/dL is significantly reduced, both for announced meals using bolus compensation and unannounced meals with a meal detection algorithm triggered bolus. No differences in the average insulin delivered were observed between the adaptive design and the conservative or aggressive tuning for the bolus strategy, and the adaptive controller delivered less insulin in the other scenario considered. The adaptive strategy provides safe and effective glucose management as well as significant reduction of hypoglycemia events. No abnormal insulin delivery profiles were observed upon the application of the adaptive strategy.

Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control.

Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both in silico tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity. Next, the asymmetric nature of the short-term consequences of hypoglycemia versus hyperglycemia is incorporated in an asymmetric weighting of the MPC cost function. Finally, an enhanced dynamic insulin-on-board algorithm is proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of blood glucose due to rescue carbohydrate load with accompanying insulin suspension. Each advancement is evaluated separately and in unison through in silico trials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in an advisory mode (simulated) testing of clinical data. The combination of the three proposed advancements show statistically significantly improved performance over the nonpersonalized controller without any enhancements across all metrics, displaying increased time in the 70-180 mg/dL safe glycemic range (76.9 versus 68.8%) and the 80-140 mg/dL euglycemic range (48.1 versus 44.5%), without a statistically significant increase in instances of hypoglycemia. The proposed advancements provide safe control action for AP applications, personalizing and improving controller performance without the need for extensive model identification processes.

Weight-Transducerless Rollback Mitigation Adopting Enhanced MPC With Extended State Observer for Direct-Drive Elevators

The permanent magnet traction machine has become the tendency of modern gearless elevators, because of small volume, high efficiency, and good dynamics. However, the direct-drive elevator car will slide during the electromagnetic brake releasing at elevator startup, if the speed controller cannot generate a suitable electromagnetic torque to trace the change of the uncertain disturbance on traction sheave. To attenuate the car sliding of the gearless elevator installed with an ordinary resolution encoder, a weight-transducerless rollback mitigation strategy adopting an enhanced model predictive control with extended state observer (ESO) for speed loop is proposed. The ESO is used to overcome the mismatch of predictive model caused by the braking torque and the unknown car load. Both the stability and the parameter selection of the observer are analyzed. Additionally, a nonlinear tracking differentiator is used to process the raw speed signal generated by the conventional speed differential sampling instead of the low-pass filter, which can overcome the vibration of the elevator car when the brake releases. Finally, experimental results verify that the proposed control strategy can achieve shorter sliding distance and smaller sliding speed for the direct-drive elevator installed with an ordinary-resolution encoder.

Temperature Control of Industrial Coke Furnace Using Novel State Space Model Predictive Control

This paper proposes an enhanced model predictive control (MPC) using a new state space structure for temperature control of an industrial coke furnace. The advantage of the proposed controller lies in the fact that its implementation only requires a simple step-response process model, whereas controller design can be based on state space formulation to improve temperature regulation. To ensure control performance effectiveness under model/process mismatch and uncertainties, model predictions and the cost function optimization are done on the basis of a new improved state space model. The proposed MPC is applied to an industrial coke furnace, where the outlet temperature in the radiation room is regulated. Simulation comparisons with traditional state space MPC are illustrated first. Then experimental results are shown in comparison with the original proportional-integral differential (PID) controller, demonstrating the effectiveness of the proposed methodology.

Glucose control in intensive care: usability, efficacy and safety of Space GlucoseControl in two medical European intensive care units.

BackgroundThe Space GlucoseControl system (SGC) is a nurse-driven, computer-assisted device for glycemic control combining infusion pumps with the enhanced Model Predictive Control algorithm (B. Braun, Melsungen, Germany). We aimed to investigate the performance of the SGC in medical critically ill patients.MethodsTwo open clinical investigations in tertiary centers in Graz, Austria and Zurich, Switzerland were performed. Efficacy was assessed by percentage of time within the target range (4.4-8.3 mmol/L; primary end point), mean blood glucose, and sampling interval. Safety was assessed by the number of hypoglycemic episodes (≤2.2 mmol/L) and the percentage of time spent below this cutoff level. Usability was analyzed with a standardized questionnaire given to involved nursing staff after the trial.ResultsForty medical critically ill patients (age, 62 ± 15 years; body mass index, 30.0 ± 8.9 kg/m2; APACHE II score, 24.8 ± 5.4; 27 males; 8 with diabetes) were included for a period of 6.5 ± 3.7 days (n = 20 in each center). The primary endpoint (time in target range 4.4 to 8.3 mmol/l) was reached in 88.3% ± 9.3 of the time and mean arterial blood glucose was 6.7 ± 0.4 mmol/l. The sampling interval was 2.2 ± 0.4 hours. The mean daily insulin dose was 87.2 ± 64.6 IU. The adherence to the given insulin dose advice was high (98.2%). While the percentage of time spent in a moderately hypoglycemic range (2.2 to 3.3 mmol/L) was low (0.07 ± 0.26% of the time), one severe hypoglycemic episode (<2.2 mmol/L) occurred (2.5% of patients or 0.03% of glucose readings).ConclusionsSGC is a safe and efficient method to control blood glucose in critically ill patients as assessed in two European medical intensive care units.

Evaluation of blood glucose control in ICU patients with Space GlucoseControl: a European study

Regardless of the ongoing debate on optimum target ranges, glycaemia control (GC) remains an important therapeutic goal in critically ill patients. Dozens of different insulin protocols for ICUs have been developed with different complexity, effectiveness, blood glucose (BG) variability and safety. Although comparison of existing protocols is difficult due to significant differences in processes and outcome measures, computerized clinical decision support systems generally achieved better GC with consistently lower hypoglycaemia rates than that achieved with paper-based protocols [1]. The enhanced Model Predictive Control (eMPC) algorithm, developed by the CLINICIP group, is the effective clinically proven protocol, which has been successfully tested at multiple institutions on medical and surgical patients with different nutritional protocols [2,3]. The eMPC models the behaviour of glucose and insulin in ICU patients with a variable sample interval based on the accuracy of the BG prediction. It has been integrated in the B.Braun Space GlucoseControl (SGC) system, which allows direct data communication between pumps and Space Control with the incorporated eMPC algorithm. Although SGC is already clinically used in dozens of ICUs worldwide, there are few only published experiences with its use [4].

The Use of Continuous Glucose Monitoring Combined with Computer-Based eMPC Algorithm for Tight Glucose Control in Cardiosurgical ICU

Aim. In postcardiac surgery patients, we assessed the performance of a system for intensive intravenous insulin therapy using continuous glucose monitoring (CGM) and enhanced model predictive control (eMPC) algorithm. Methods. Glucose control in eMPC-CGM group (n = 12) was compared with a control (C) group (n = 12) treated by intravenous insulin infusion adjusted according to eMPC protocol with a variable sampling interval alone. In the eMPC-CGM group glucose measured with a REAL-Time CGM system (Guardian RT) served as input for the eMPC adjusting insulin infusion every 15 minutes. The accuracy of CGM was evaluated hourly using reference arterial glucose and Clarke error-grid analysis (C-EGA). Target glucose range was 4.4–6.1 mmol/L. Results. Of the 277 paired CGM-reference glycemic values, 270 (97.5%) were in clinically acceptable zones of C-EGA and only 7 (2.5%) were in unacceptable D zone. Glucose control in eMPC-CGM group was comparable to C group in all measured values (average glycemia, percentage of time above, within, and below target range,). No episode of hypoglycemia (<2.9 mmol) occurred in eMPC-CGM group compared to 2 in C group. Conclusion. Our data show that the combination of eMPC algorithm with CGM is reliable and accurate enough to test this approach in a larger study population.

Cardiometabolic Diseases and Related Complications: Current Status and Future Perspective

Cardiometabolic Diseases and Related Complications: Current Status and Future Perspective

New Flexure Parallel-Kinematic Micropositioning System With Large Workspace

Flexure-based micropositioning systems with a large workspace are attractive for a variety of precision engineering applications. In this paper, a new idea of multistage compound parallelogram flexure is proposed for the mechanism design of a novel parallel-kinematic XY micropositioning system, which has a motion range larger than 10 mm along with a compact structure. The established quantitative models and the stage performances are validated by conducting finite-element analysis (FEA) and experimental studies. Moreover, an enhanced model-predictive control (EMPC) is presented for positioning control of the system, which has a nonminimum-phase plant. It is shown that the EMPC is capable of producing a low magnitude of output tracking error by imposing an appropriate suppression on the control effort. Simulation and experimental studies reveal that the EMPC scheme outperforms the conventional proportional-integral-derivative (PID) and MPC methods in terms of transient response speed and steady-state accuracy. The idea that is presented in this paper is extendable to design and control of other micro-/nanopositioning systems with either minimum- or nonminimum-phase plants.