The design of periodic excitations for dynamic system identification

System identification techniques are developed for modelling linear and nonlinear systems. The main results of the work are concerned with the design and utilisation of periodic perturbation signals in general areas of time- and frequency-domain system identification. A design strategy is given for a new class of perturbation signals, together with examples of their use in system identification applications. Signal processing procedures are developed for the practical treatment of drift disturbances and transient effects, and also for the detection of nonlinear contributions to the measurement data. The techniques rely completely on the periodicity of the excitation, and so the advantageous properties of periodic input signals are considered in detail. The use of periodic excitations in discrete- and continuous-time nonlinear system identification is also reported, with the identification methods illustrating the worth of frequency-domain measurements in this area. An automatic tuning procedure for PID controllers is also developed, which illustrates an application of system identification techniques to control problems

HD Physiology Project-Japanese efforts to promote multilevel integrative systems biology and physiome research.

The HD Physiology Project is a Japanese research consortium that aimed to develop methods and a computational platform in which physiological and pathological information can be described in high-level definitions across multiple scales of time and size. During the 5 years of this project, an appropriate software platform for multilevel functional simulation was developed and a whole-heart model including pharmacokinetics for the assessment of the proarrhythmic risk of drugs was developed. In this article, we outline the description and scientific strategy of this project and present the achievements and influence on multilevel integrative systems biology and physiome research

Parametric Macromodels of Digital I/O Ports

This paper addresses the development of macromodels for input and output ports of a digital device. The proposed macromodels consist of parametric representations that can be obtained from port transient waveforms at the device ports via a well established procedure. The models are implementable as SPICE subcircuits and their accuracy and efficiency are verified by applying the approach to the characterization of transistor-level models of commercial devices

Optimal input design and parameter estimation for continuous-time dynamical systems

Diese Arbeit behandelt die Themengebiete Design of Experiments (DoE) und Parameterschätzung für zeitkontinuierliche Systeme, welche in der modernen Regelungstheorie eine wichtige Rolle spielen. Im gewählten Kontext untersucht DoE die Auswirkungen von verschiedenen Rahmenbedingungen von Simulations- bzw. Messexperimenten auf die Qualität der Parameterschätzung, wobei der Fokus auf der Anwendung der Theorie auf praxisrelevante Problemstellungen liegt. Dafür wird die weithin bekannte Fisher-Matrix eingeführt und die resultierende nicht lineare Optimierungsaufgabe angeschrieben. An einem PT1-System wird der Informationsgehalt von Signalen und dessen Auswirkungen auf die Parameterschätzung gezeigt. Danach konzentriert sich die Arbeit auf ein Teilgebiet von DoE, nämlich Optimal Input Design (OID), und wird am Beispiel eines 1D-Positioniersystems im Detail untersucht. Ein Vergleich mit häufig verwendeten Anregungssignalen zeigt, dass generierte Anregungssignale (OID) oft einen höheren Informationsgehalt aufweisen und mit genaueren Schätzwerten einhergeht. Zusätzlicher Benefit ist, dass Beschränkungen an Eingangs-, Ausgangs- und Zustandsgrößen einfach in die Optimierungsaufgabe integriert werden können. Der zweite Teil der Arbeit behandelt Methoden zur Parameterschätzung von zeitkontinuierlichen Modellen mit dem Fokus auf der Verwendung von Modulationsfunktionen (MF) bzw. Poisson-Moment Functionals (PMF) zur Vermeidung der zeitlichen Ableitungen und Least-Squares zur Lösung des resultierenden überbestimmten Gleichungssystems. Bei verrauschten Messsignalen ergibt sich daraus sofort die Problematik von nicht erwartungstreuen Schätzergebnissen (Bias). Aus diesem Grund werden Methoden zur Schätzung und Kompensation von Bias Termen diskutiert. Beitrag dieser Arbeit ist vor allem die detaillierte Aufarbeitung eines Ansatzes zur Biaskompensation bei Verwendung von PMF und Least-Squares für lineare Systeme und dessen Erweiterung auf (leicht) nicht lineare Systeme. Der vorgestellte Ansatz zur Biaskompensation (BC-OLS) wird am nicht linearen 1D-Servo in der Simulation und mit Messdaten validiert und in der Simulation mit anderen Methoden, z.B., Total-Least-Squares verglichen. Zusätzlich wird der Ansatz von PMF auf die weiter gefasste Systemklasse der Modulationsfunktionen (MF) erweitert. Des Weiteren wird ein praxisrelevantes Problem der Parameteridentifikation diskutiert, welches auftritt, wenn das Systemverhalten nicht gänzlich von der Identifikationsgleichung beschrieben wird. Am 1D-Servo wird gezeigt, dass ein Deaktivieren und Reaktivieren der PMF Filter mit geeigneter Initialisierung diese Problematik einfach löst.This thesis addresses two topics that play a significant role in modern control theory: design of experiments (DoE) and parameter estimation methods for continuous-time (CT) models. In this context, DoE focuses on the impact of experimental design regarding the accuracy of a subsequent estimation of unknown model parameters and applying the theory to real-world applications and its detailed analysis. We introduce the Fisher-information matrix (FIM), consisting of the parameter sensitivities and the resulting highly nonlinear optimization task. By a first-order system, we demonstrate the computation of the information content, its visualization, and an illustration of the effects of higher Fisher information on parameter estimation quality. After that, the topic optimal input design (OID), a subarea of DoE, will be thoroughly explored on the practice-relevant linear and nonlinear model of a 1D-position servo system. Comparison with standard excitation signals shows that the OID signals generally provide higher information content and lead to more accurate parameter estimates using least-squares methods. Besides, this approach allows taking into account constraints on input, output, and state variables. In the second major topic of this thesis, we treat parameter estimation methods for CT systems, which provide several advantages to identify discrete-time (DT) systems, e.g., allows physical insight into model parameters. We focus on modulating function method (MFM) or Poisson moment functionals (PMF) and least-squares to estimate unknown model parameters. In the case of noisy measurement data, the problem of biased parameter estimation arises immediately. That is why we discuss the computation and compensation of the so-called estimation bias in detail. Besides the detailed elaboration of a bias compensating estimation method, this work’s main contribution is, based on PMF and least squares for linear systems, the extension to at least slightly nonlinear systems. The derived bias-compensated ordinary least-squares (BCOLS) approach for obtaining asymptotically unbiased parameter estimates is tested on a nonlinear 1D-servo model in the simulation and measurement. A comparison with other methods for bias compensation or avoidance, e.g., total least-squares (TLS), is performed. Additionally, the BC-OLS method is applied to the more general MFM. Furthermore, a practical issue of parameter estimation is discussed, which occurs when the system behavior leaves and re-enters the space covered by the identification equation. Using the 1D-servo system, one can show that disabling and re-enabling the PMF filters with appropriate initialization can solve this problem

Development and identification of hierarchical nonlinear mixed effects models for the analysis of dynamic systems: identification and application of hierarchical nonlinear mixed effects models for the determination of steady-state and dynamic torque responses of an SI engine

Multi-level or hierarchical models present various features for dealing with data grouped at several levels. The majority of applications of hierarchical models use clustered data that is static in nature and collected over a long period of time. The purpose of this study is investigating hierarchical models for application with highly dynamic systems. Steady-state data are conventionally employed for engine torque mapping purposes. The data takes much time to collect and the dynamics of the system are routinely ignored. This valuable information could be used for better control of the system.In this study, an innovative transient spark-sweep approach is developed for collecting dynamic torque data more efficiently. The means of data collection implies a structure for which a multi-level model is best suited. A multi-model augmented D-optimal design is created, and the experimental data collected. Spark excitation is applied at speed/load points using Amplitude Modulated Pseudo Random Signal (AMPRS), and the torque response over the operating space is thus obtained. Conditional first-order linearization is used within the identification process for determining the hierarchical model parameters. The level-1 Nonlinear Auto Regressive eXogenous (NARX) models are separately determined using an Iterative Generalized Least Square (IGLS) method and the results are employed for initialisation of the covariance matrix and the model level-2 parameters. A novel gradient optimiser was established to facilitate the dynamic hierarchical model identification. Additionally, the uncertainty associated with model selection was mitigated using a multi-model approach. The model identified is evaluated and compared with experimental dynamic and steady-state data. It shows behaviour, both dynamic and steady state, providing prediction over a wider extrapolated spark range than conventional approaches. The new approach is eight time faster than current state-of-the-art approaches.</div

Aeronautical Engineering: A special bibliography with indexes, supplement 48

This special bibliography lists 291 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1974