Christoph Ament

High Precision Position Control Using an Adaptive Friction Compensation Approach

The presented work concerns the development of a trajectory tracking controller which is able to improve clearly the dynamical performance of a high precision positioning stage. Experiments in the pre-rolling and rolling friction regimes are conducted and a hybrid parameter estimation algorithm is used to fit the parameters of a simple dynamic friction model based on experimental data. Further experiments show that the identified model does not represent the system behavior over the whole operating range of 200 mm. To solve this problem the linear model parameters are adjusted online to ensure precise dynamic friction compensation. Finally, the extended friction model is utilized in a feed-forward controller in combination with a standard feedback controller to compensate for the effects of the friction force and other disturbances while moving.

The Unscented Kalman Filter estimates the plasma insulin from glucose measurement

Understanding the simultaneous interaction within the glucose and insulin homeostasis in real-time is very important for clinical treatment as well as for research issues. Until now only plasma glucose concentrations can be measured in real-time. To support a secure, effective and rapid treatment e.g. of diabetes a real-time estimation of plasma insulin would be of great value. A novel approach using an Unscented Kalman Filter that provides an estimate of the current plasma insulin concentration is presented, which operates on the measurement of the plasma glucose and Bergmans Minimal Model of the glucose insulin homeostasis. We can prove that process observability is obtained in this case. Hence, a successful estimator design is possible. Since the process is nonlinear we have to consider estimates that are not normally distributed. The symmetric Unscented Kalman Filter (UKF) will perform best compared to other estimator approaches as the Extended Kalman Filter (EKF), the simplex Unscented Kalman Filter (UKF), and the Particle Filter (PF). The symmetric UKF algorithm is applied to the plasma insulin estimation. It shows better results compared to the direct (open loop) estimation that uses a model of the insulin subsystem.

Asymmetric motion profile planning for nanopositioning and nanomeasuring machines

Abstract This work presents an analytic fourth-order trajectory planning algorithm, which is able to plan asymmetric motions with arbitrary initial and final velocities. Furthermore, the proposed algorithm is based on a set of quadratic derivates of jerk (djerk) functions and generates continuously differentiable trajectories in jerk, acceleration, velocity, and position under consideration of kinematic constraints in all these kinematical values. The trajectories planned by the algorithm also have time-optimal characteristics, and a synchronization between the three motion axes of the Cartesian coordinate system is ensured by the proposed method. These characteristics make it ideally suited for use as a trajectory planning algorithm in high-precision applications such as nanopositioning and nanomeasuring machines.

Real-time state estimation and long-term model adaptation: a two-sided approach toward personalized diagnosis of glucose and insulin levels.

Background: With continuous glucose sensors (CGSs), it is possible to obtain a dynamical signal of the patients subcutaneous glucose concentration in real time. How could that information be exploited? We suggest a model-based diagnosis system with a twofold objective: real-time state estimation and long-term model parameter identification. Methods: To obtain a dynamical model, Bergmans nonlinear minimal model (considering plasma glucose G, insulin I, and interstitial insulin X) is extended by two states describing first and second insulin response. Furthermore, compartments for oral glucose and subcutaneous insulin inputs as well as for subcutaneous glucose measurement are added. The observability of states and external inputs as well as the identifiability of model parameters are assessed using the empirical observability Gramian. Signals are estimated for different nondiabetic and diabetic scenarios by unscented Kalman filter. Results: (1) Observability of different state subsets is evaluated, e.g., from CGSs, { G, I} or { G, X} can be observed and the set { G, I, X} cannot. (2) Model parameters are included, e.g., it is possible to estimate the second-phase insulin response gain k G2 additionally. This can be used for model adaptation and as a diagnostic parameter that is almost zero for diabetes patients. (3) External inputs are considered, e.g., oral glucose is theoretically observable for nondiabetic patients, but estimation scenarios show that the time delay of 1 h limits application. Conclusions: A real-time estimation of states (such as plasma insulin I) and parameters (such as k G2) is possible, which allows an improved real-time state prediction and a personalized model.

Diabetic and metabolic programming: mechanisms altering the intrauterine milieu.

A wealth of epidemiological, clinical, and experimental studies have been linked to poor intrauterine conditions as well as metabolic and associated cardiovascular changes postnatal. These are novel perspectives connecting the altered intrauterine milieu to a rising number of metabolic diseases, such as diabetes, obesity, and hypercholesterolemia as well as the Metabolic Syndrome (Met S). Moreover, metabolic associated atherosclerotic diseases are connected to perigestational maternal health. The “Thrifty Phenotype Hypothesis” introduced cross-generational links between poor conditions during gestation and metabolic as well as cardiovascular alterations postnatal. Still, mechanisms altering the intrauterine milieu causing metabolic and associated atherosclerotic diseases are currently poorly understood. This paper will give novel insights in fundamental concepts connected to specific molecular mechanisms “programming” diabetes and associated metabolic as well as cardiovascular diseases.

Identifiability and online estimation of diagnostic parameters with in the glucose insulin homeostasis

Today, diagnostic decisions about pre-diabetes or diabetes are made using static threshold rules for the measured plasma glucose. In order to develop an alternative diagnostic approach, dynamic models as the Minimal Model may be deployed. We present a novel method to analyze the identifiability of model parameters based on the interpretation of the empirical observability Gramian. This allows a unifying view of both, the observability of the systems states (with dynamics) and the identifiability of the systems parameters (without dynamics). We give an iterative algorithm, in order to find an optimized set of states and parameters to be estimated. For this set, estimation results using an Unscented Kalman Filter (UKF) are presented. Two parameters are of special interest for diagnostic purposes: the glucose effectiveness S(G) characterizes the ability of plasma glucose clearance, and the insulin sensitivity S(I) quantifies the impact from the plasma insulin to the interstitial insulin subsystem. Applying the identifiability analysis to the trajectories of the insulin glucose system during an intravenous glucose tolerance test (IVGTT) shows the following result: (1) if only plasma glucose G(t) is measured, plasma insulin I(t) and S(G) can be estimated, but not S(I). (2) If plasma insulin I(t) is captured additionally, identifiability is improved significantly such that up to four model parameters can be estimated including S(I). (3) The situation of the first case can be improved, if a controlled external dosage of insulin is applied. Then, parameters of the insulin subsystem can be identified approximately from measurement of plasma glucose G(t) only.

A Novel Mathematical Model Detecting Early Individual Changes of Insulin Resistance

BACKGROUNDnInsulin resistance (IR) and hyperinsulinemia as well as obesity play a key role in the metabolic syndrome (MetS), type 2 diabetes (T2D), and associated cardiovascular disease. Unfortunately, IR and hyperinsulinemia are often diagnosed late (i.e., when the MetS is already clinically evident). An earlier diagnosis of IR would be desirable to reduce its clinical consequences, in particular in view of the increasing prevalence of obesity and diabetes conditions. For this purpose, we developed a mathematical model capable of detecting early onset of IR through small variations of insulin sensitivity, glucose effectiveness, and first- or second-phase responses.nnnMATERIALS AND METHODSnMurine models provide controlled conditions to study various stages of IR. Various degrees of hypercholesterolemia, obesity, IR, and atherosclerosis were induced in low-density lipoprotein receptor-deficient mice by feeding them cholesterol- or sucrose-rich diets. IR was assessed by oral glucose tolerance tests. Controls included animals fed exclusively, or switched back to, regular chow. A nonlinear mathematical model of the order of 5 was developed by refining Bergmans Minimal Model and then applied to experimental data.nnnRESULTSnDifferent metabolic constellations consistently corresponded to specific and close-meshed changes in model parameters. Reduced second-phase glucose sensitivity characterized an early impaired glucose tolerance. Later stages showed an increased first-phase glucose sensitivity compensating for decreased insulin sensitivity. Finally, T2D was associated with both first- and second-phase sensitivities close to zero.nnnCONCLUSIONSnThe new mathematical model detected various insulin-sensitive or -resistant metabolic stages of IR. It can therefore be implemented for quantitative metabolic risk assessment and may be of therapeutic value by anticipating the start of therapeutic interventions.

Vergleich der Scan-Performance bei Nanopositioniersystemen mit großem Bewegungsbereich

Zusammenfassung In der vorliegenden Arbeit werden zwei Nanopositioniersysteme in Bezug auf ihre Positioniergenauigkeit während der Bewegung verglichen. Beide Systeme besitzen einen planaren Bewegungsbereich von ≥u2009100u2009mm, werden durch Linearmotoren angetrieben und die Position wird durch Laserinterferometer gemessen. Große Unterschiede existieren jedoch im mechanischen Aufbau. Das erste Positioniersystem ist ein zweiachsiger Demonstrator dessen zwei Läufer auf Wälzkörpern gelagert sind. Dies führt zu dem Problem der besonders im Nanometerbereich stark nichtlinearen Reibung. Bei dem zweiten Positioniersystem kommen Luftlager zum Einsatz und darüber hinaus handelt es sich nur um einen Läufer, welcher drei Freiheitsgrade besitzt. Es wird gezeigt, dass durch regelungstechnische Methoden der Reibkraftkompensation die Positioniergenauigkeit beider Systeme bis zu einer Geschwindigkeit von ca. 1u2009mm/s vergleichbar ist.

Identification of tissue differentiation rates in a mechanobiological model of fracture healing

As a basis for model-based analysis of the processes in secondary fracture healing, a dynamical model is presented that characterises the physiological status in the fracture area by the location-dependent composition of tissues. Five types of tissue are distinguished: connective tissue, cartilage, bone, haematoma and avascular bone. A rule base is given that describes dynamical tissue differentiation processes. The rules consider not only a mechanical stimulus but also osteogenic and a vasculative factors as biological stimuli. Within this model structure, it is possible, e.g., to distinguish intramembranous from endochondral ossification processes. An objective function is introduced to assess accordance between the model-based simulation results and reference healing stages. By minimising this objective function, relevant tissue differentiation rates can be determined. For a reference process of secondary fracture healing it could be shown that the intramembranous ossification rate of 0.313%/day (from connective tissue to bone) is much smaller than the endochondral ossification rate of 1.136%/day (from cartilage to bone). In order to verify the model approach, it is transferred to simulate long bone distraction. Results show that healing patterns of bone distraction can be predicted. Using this method, it is possible to identify model parameters for individual subjects. This will allow a patient-specific analysis of tissue healing processes in future.

Novel individual metabolic profile characterizes the protein kinase B-alpha (pkbα−/−) in vivo model

Abstract Context: Type 2 diabetes and associated co-morbidities run epidemic waves worldwide. Since pathophysiological constellations are individual and display a wide spread of dysmetabolic profiles personalized health care assessments start to emerge. Therefore, we established a specific in silico assessment tool targeting metabolic characterizations individually. Materials and methods: Values obtained from oral glucose and intraperitoneal insulin tolerance tests performed on pkbα−/− mice (KO) as well as age- and gender-matched controls (WT) were analysed using our established in silico model. Results: Generally, male pkbα−/− mice (KO) presented significantly increased insulin sensitivity at an age of 6 months compared with age-matched WTs (pu2009=u20090.036). Female KO and WT groups displayed improved glucose sensitivities compared with age-matched males (for WT pu2009≤u20090.011). Discussion and conclusion: Specific metabolic characterization should be assessed individually. Therefore, our in silico model enables novel insights inaugurating specific primary preventive strategies targeting individual metabolic profiling precisely.