Luigi del Re

Predictive control of a real-world Diesel engine using an extended online active set strategy

Abstract In order to meet tight emission limits Diesel engines are nowadays equipped with additional hardware components like an exhaust gas recirculation valve and a variable geometry turbocharger. Conventional engine control units use two SISO control loops to regulate the exhaust gas recirculation valve and the variable geometry turbocharger, although their effects are highly coupled. Moreover, these actuators are subject to physical constraints which seems to make an advanced control approach like model predictive control (MPC) the method of choice. In order to deal with MPC sampling times in the order of milliseconds, we employed an extension of the recently developed online active set strategy for controlling a real-world Diesel engine in a closed-loop manner. The results show that predictive engine control based on online optimisation can be accomplished in real-time – even on cheap controller hardware – and leads to increased controller performance.

Performance evaluations of continuous glucose monitoring systems: precision absolute relative deviation is part of the assessment.

Background: Even though a Clinical and Laboratory Standards Institute proposal exists on the design of studies and performance criteria for continuous glucose monitoring (CGM) systems, it has not yet led to a consistent evaluation of different systems, as no consensus has been reached on the reference method to evaluate them or on acceptance levels. As a consequence, performance assessment of CGM systems tends to be inconclusive, and a comparison of the outcome of different studies is difficult. Materials and Methods: Published information and available data (as presented in this issue of Journal of Diabetes Science and Technology by Freckmann and coauthors) are used to assess the suitability of several frequently used methods [International Organization for Standardization, continuous glucose error grid analysis, mean absolute relative deviation (MARD), precision absolute relative deviation (PARD)] when assessing performance of CGM systems in terms of accuracy and precision. Results: The combined use of MARD and PARD seems to allow for better characterization of sensor performance. The use of different quantities for calibration and evaluation, e.g., capillary blood using a blood glucose (BG) meter versus venous blood using a laboratory measurement, introduces an additional error source. Using BG values measured in more or less large intervals as the only reference leads to a significant loss of information in comparison with the continuous sensor signal and possibly to an erroneous estimation of sensor performance during swings. Both can be improved using data from two identical CGM sensors worn by the same patient in parallel. Conclusions: Evaluation of CGM performance studies should follow an identical study design, including sufficient swings in glycemia. At least a part of the study participants should wear two identical CGM sensors in parallel. All data available should be used for evaluation, both by MARD and PARD, a good PARD value being a precondition to trust a good MARD value. Results should be analyzed and presented separately for clinically different categories, e.g., hypoglycemia, exercise, or night and day.

A model predictive Cooperative Adaptive Cruise Control approach

Reduction of fuel consumption is one of the primary goals of modern automotive engineering. While in the past the focus was on more efficient engine design and control there is an upcoming interest on economic context aware control of the complete vehicle. Technical progress will enable future vehicles to interact with other traffic participants and the surrounding infrastructure, collecting information which allow for reduction of fuel consumption by predictive vehicle control strategies. The principle of Model Predictive Control allows a straightforward integration of e.g. navigation systems, on-board radar sensors, V2V- and V2I-communication whilst regarding constraints and dynamic of the system. This paper presents a Linear Model Predictive Control approach to Cooperative Adaptive Cruise Control, directly minimizing the fuel consumption rather than the acceleration of the vehicle. To this end the nonlinear static fuel consumption map of the internal combustion engine is included into the control design by a piecewise quadratic approximation. Inclusion of a linear spacing policy prevents rear end collisions. Simulation results demonstrate the fuel and road capacity benefits, for a single vehicle and for a string of vehicles, equipped with the proposed control, in comparison to vehicles operated by a non-cooperative adaptive cruise control. Full information on the speed prediction of the predecessor is assumed, hence the purpose of this paper is twofold. On the one hand, best achievable benefits, of the proposed control, due to perfect prediction are demonstrated. On the other hand, the paper studies the behavior of the considered control and the influence of the prediction horizon.

Estimating Interval Process Models for Type 1 Diabetes for Robust Control Design

Abstract A linear transfer function model comprising 4 parameters is used to describe the post-prandial breakfast excursions of a group of 10 type 1 diabetes patients who are treated with multiple daily insulin injections. The model is able to simulate the glucose concentration in blood and uses the information of carbohydrate content of the breakfast and the administered insulin injections as inputs. Additionally, a measurement of the actual blood glucose concentration at the time when the breakfast occurs is required. No additional information, in particular the use of a continuous glucose monitoring system is necessary. Based on a 3 day observational period, parameter intervals are calculated such that the measured glucose responses are inside the bounds given by the output of the model. The model together with the parameter intervals can be used for robust control design.

NOx Virtual Sensor Based on Structure Identification and Global Optimization

On-line measurement of engine NOx emissions is the object of a substantial effort, as it would strongly improve the control of Cl engines. Many efforts have been directed towards hardware solutions, in particular to physical sensors, which have already reached a certain degree of maturity. In this paper, we are concerned with an alternative approach, a virtual sensor, which is essentially a software code able to estimate the correct value of an unmeasured variable, thus including in some sense an input/output model of the process. Most virtual sensors are either derived by fitting data to a generic structure (like an artificial neural network, ANN) or by physical principles. In both cases, the quality of the sensor tends to be poor outside the measured values. In this paper, we present a new approach: the data are screened for hidden analytical structures, combining structure identification and evolutionary algorithms, and these structures are then used to develop the sensor presented. While the computational time for the sensor design can be significant (e.g. 1 or more hours), the resulting formula is very compact and proves able to predict the behaviour of the system at other operating points. The method has been validated with NOx data from a production engine measured with a Horiba Mexa 7000. The approach is able to yield a good prediction behaviour over a whole cycle. The results are consistent with physical knowledge.

ON PERSISTENT EXCITATION FOR PARAMETER ESTIMATION OF QUASI-LPV SYSTEMS AND ITS APPLICATION IN MODELING OF DIESEL ENGINE TORQUE

Abstract In this paper, necessary and sufficient conditions for the identifiability of a class of discrete quasi-LPV (linear parameter varying) systems are given and an application in the field of diesel engine modeling is shown. To obtain our results, a Kronecker product framework is used.

A diabetes management system empowering patients to reach optimised glucose control: From monitor to advisor

The DIAdvisor™ is an EC/FP7 funded project aiming at the development of a Blood Glucose prediction device which uses easily available information to optimise the therapy of patients with diabetes.

Grey-Box Control Oriented Emissions Models

Abstract Further improvements of emission control will require reliable estimation of emissions in real time. While many progresses are being done in terms of physical sensors, there is a wide agreement that virtual sensors and more in general real time emission models will play a central role in the next steps. While there is a deep understanding of the physics of the regulated pollutants, most general emission models tend to be too complex and poorly parametrized to be used on-line, while most data based models tend to be either insufficiently precise or of limited scope. To avoid this problem, this paper proposes a combined approach in which static maps are identified numerically, but the effect of dominant factors, like cylinder-head temperature and air path dynamics, is included on the basis of physical assumptions. Differently from most models developed for sensors, this approach is based on pure engine control unit ( ECU ) data, i.e. can be used for the computation of optimal control laws. As the paper shows, this strategy is able to provide not only real time estimation of NO x as a function of the ECU outputs, but also of particulate matter ( PM ).

Time Delay of CGM Sensors: Relevance, Causes, and Countermeasures

Background: Continuous glucose monitoring (CGM) is a powerful tool to support the optimization of glucose control of patients with diabetes. However, CGM systems measure glucose in interstitial fluid but not in blood. Rapid changes in one compartment are not accompanied by similar changes in the other, but follow with some delay. Such time delays hamper detection of, for example, hypoglycemic events. Our aim is to discuss the causes and extent of time delays and approaches to compensate for these. Methods: CGM data were obtained in a clinical study with 37 patients with a prototype glucose sensor. The study was divided into 5 phases over 2 years. In all, 8 patients participated in 2 phases separated by 8 months. A total number of 108 CGM data sets including raw signals were used for data analysis and were processed by statistical methods to obtain estimates of the time delay. Results: Overall mean (SD) time delay of the raw signals with respect to blood glucose was 9.5 (3.7) min, median was 9 min (interquartile range 4 min). Analysis of time delays observed in the same patients separated by 8 months suggests a patient dependent delay. No significant correlation was observed between delay and anamnestic or anthropometric data. The use of a prediction algorithm reduced the delay by 4 minutes on average. Conclusions: Prediction algorithms should be used to provide real-time CGM readings more consistent with simultaneous measurements by SMBG. Patient specificity may play an important role in improving prediction quality.

Cooperative adaptive cruise control applying stochastic linear model predictive control strategies

In this paper a cooperative adaptive cruise control approach using stochastic, linear model predictive control strategies is presented. The presented approach deals with an urban traffic environment where vehicle to vehicle and vehicle to infrastructure communication systems are available. The goal is the minimization of a vehicles fuel consumption in a vehicle-following scenario. This is achieved by minimizing a piecewise linear approximation of the vehicles fuel consumption map. By means of a conditional Gaussian model the probability distribution of the upcoming velocity of the preceding vehicle is estimated based on current measurements and upcoming traffic light signals. The predicted distribution function of the predecessors velocity is used in two ways for stochastic model predictive control. On the one hand, individual chance constraints are introduced and subsequently reformulated to obtain an equivalent deterministic model predictive control problem. On the other hand, samples are drawn from the prediction model and used for a randomized optimization approach. Finally, the two developed stochastic control strategies are evaluated and compared against a deterministic model predictive control approach by means of a virtual traffic simulation. The evaluation of the controllers show a significant reduction of the fuel consumption compared to the predecessor while increasing safety and driving comfort.