In this contribution, we present an abstract second-order mean value engine model suitable for model-based control and parameter estimation methods. Furthermore, we introduce an online capable algorithm for flex-fuel identification, i.e., ethanol content estimation, based on air-to-fuel (AFR) control. Simulation results show, that the algorithm can reliably estimate the ethanol content, and provides additional monitoring functionality about deviations in the amount of injected fuel.

We present a model-based approach for the instant detection of faults on the DC side of photovoltaic (PV) systems. The algorithm does not identify the faults itself, but estimates the nominal PV system behavior, i.e. system parameters, using simple PV and line models. Sudden deviations from the expected model behavior serve as an indicator for the ignition of a fault. To ensure that the PV model parameters can be estimated, an identifiability analysis has to be performed. The performance of the algorithm is demonstrated exemplarily by the detection of serial electric arcs in PV systems. Measurement results show that all series arc faults are successfully detected. There are no false detections due to maximum power point tracking (MPPT) operations or environmental influences like shading, changes in solar irradiation, etc. The main advantages of the presented method are less computational effort, resulting in very fast detection times, and its flexible integration into existing systems.

In course of the continuing trend towards lightweight construction of fast positioning systems, cable feeders, sensor connections, power supply, etc., may have noticeable influence on the system behavior. To study the effects of such additional masses a multi-body model of a positioning drive, serving as a reference model, is built and compared with measurements. Since it turns out that this multi-body model is unsuitable for the development of systems identification methods we strive for finding a proper, concentrated parametric mean value model that takes into account the variable mass. Finally, this model is used to test and evaluate standard methods for parameter identification based on Poisson moment functions.

We study optimal input design and bias-compensating parameter estimation methods for continuous-time models applied on a mechanical laboratory experiment. Within this task we compare two online estimation methods that are based on Poisson moment functions with focus on quantized system outputs due to an angular encoder: The standard recursive least-squares (RLS) approach and a bias-compensating recursive least-squares (BCRLS) approach. The rationale is to achieve acceptable estimation results in the presence of white noise, caused by low-budget encoders with low resolution. The input design and parameter estimation approaches are assessed and compared, experimentally, resorting to measurements taken from a laboratory cart system.

In this paper we present a free tool for semi‐automated matching of virtual and real prototypes in a wide range of industrial applications. Within the introduction we would like to explain the motivation behind the development of our tool. After a short description of the basic principles of input design for dynamical systems, we are focusing on the individual steps of the tool‐chain, implemented in our software. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

In this paper we present free tools for model-based optimal input design and parameter estimation. The discussed tool-chain is tailored for the needs of small- and medium sized companies. Its programming core is based on Scilab and the JModelica platform and features input design (DOE), optimal control problems (OCP), and parameter estimation. Finally, the entire tool-chain concept is tested via simulation of a cart and pendulum system.

In this study, we present an adaptive self-tuning controller (STC) design for small embedded systems. The new free tool-chain for model based control design is based, among other software, on the open simulator Scilab-XCos. After a very short introduction of model based design terms, this article focuses on the code generator and the other pro- grams of the tool-chain. The design concept is demonstrated by the non-trivial adaptive self-tuning control (STC) of the cart system in simulation and on a real laboratory experiment.

AbstractOften, trajectories for mechanical systems are generated solving some optimization problem. Common approaches include time‐optimal, energy optimal, etc., motion profiles. In order to decrease mechanical wear of real plants this profiles provide, e.g., a smooth movement (rest‐to‐rest) in accordance with restrictions in jerk, acceleration and velocity. There exists a number of methods, to calculate for a given trajectory the plant feed forward action and to design stabilizing controllers. In case of parameter uncertainty the control law often exhibits some adaptive part. Unfortunately, smooth trajectories tend to contain insufficient excitation for adaption and/or identification. Therefore, we propose to consider some measure for the information content concerning some unknown parameters in the trajectory optimization problem. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

In this study we present a new free tool-chain for model based control design for mechatronic plants applicable to small embedded systems based among other software on the open simulator Scilab-XCos. After a very short introduction of model based design terms this article focuses on the code generator and the other programs of the tool-chain. The design concept is demonstrated by an adaptive self tuning control (STC) of the cart and pendulum system in gantry crane configuration in simulation and on a real laboratory experiment.