University of Paderborn

About: University of Paderborn is a education organization based out in Paderborn, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Control reconfiguration & Software. The organization has 6684 authors who have published 16929 publications receiving 323154 citations.

Optimum Control for Interior Permanent Magnet Synchronous Motors (IPMSM) in Constant Torque and Flux Weakening Range

Abstract: Interior Permanent Magnet Synchronous Motors (IPMSM) gain importance due to their high torque per volume ratio particularly for hybrid electrical vehicles. However, unlike to standard control theory, the torque control strategy for these motors is not apparently due to their reluctance torque, which is typical with interior magnet design. In this contribution, a control strategy is presented, which enables optimal torque control both in the lower speed range as well in the full flux weakening range. Operation during flux weakening, however, causes stress to the magnets of the motor with the risk of permanent demagnetization. The relations between the crucial design parameters are shown.

Global classical small-data solutions for a three-dimensional chemotaxis Navier-Stokes system involving matrix-valued sensitivities

Abstract: The coupled chemotaxis fluid system \begin{equation} \left\{ \begin{array}{llc} n_t=\Delta n- abla\cdot(n S(x,n,c)\cdot abla c)-u\cdot abla n, &(x,t)\in \Omega\times (0,T), \displaystyle c_t=\Delta c-nc-u\cdot abla c, &(x,t)\in\Omega\times (0,T), \displaystyle u_t=\Delta u-(u\cdot abla )u+ abla P+n abla\Phi,\quad abla\cdot u=0, &(x,t)\in\Omega\times (0,T), \displaystyle abla c\cdot u=( abla n-nS(x,n,c)\cdot abla c)\cdot u=0, \;\; u=0,&(x,t)\in \partial\Omega\times (0,T), n(x,0)=n_{0}(x),\quad c(x,0)=c_{0}(x),\quad u(x,0)=u_0(x) & x\in\Omega, \end{array} \right. \end{equation} where $S\in (C^2(\bar{\Omega}\times [0,\infty)^2))^{N\times N}$, is considered in a bounded domain $\Omega\subset\mathbb{R}^N$, $N\in\{2,3\}$, with smooth boundary. We show that it has global classical solutions if the initial data satisfy certain smallness conditions and give decay properties of these solutions.

Model-based genetic algorithms for algorithm configuration

Abstract: Automatic algorithm configurators are important practical tools for improving program performance measures, such as solution time or prediction accuracy. Local search approaches in particular have proven very effective for tuning algorithms. In sequential local search, the use of predictive models has proven beneficial for obtaining good tuning results. We study the use of non-parametric models in the context of population-based algorithm configurators. We introduce a new model designed specifically for the task of predicting high-performance regions in the parameter space. Moreover, we introduce the ideas of genetic engineering of offspring as well as sexual selection of parents. Numerical results show that model-based genetic algorithms significantly improve our ability to effectively configure algorithms automatically.

Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors

Abstract: The performance of model predictive controllers (MPC) strongly depends on the quality of their models. In the field of electric drive control, white-box (WB) modeling approaches derived from first-order physical principles are most common. This procedure typically does not cover parasitic effects and often comes with parameter deviations. These issues are particularly crucial in the domain of self-commissioning drives where a hand-tailored, accurate WB plant model is not available. In order to compensate for such modeling errors and, consequently, to improve the control performance during transients and steady state, this article proposes a data-driven, real-time capable recursive least squares estimation method for the current control of a permanent magnet synchronous motor. Following this machine learning approach, the effect of the flux linkage and voltage harmonics due to the winding scheme can also be taken into account through suitable feature engineering. Moreover, a compensating scheme for the interlocking time of the inverter is proposed. The resulting algorithm is investigated using the well-known finite-control-set MPC (FCS-MPC) in the rotor-oriented coordinate system. The extensive experimental results show the superior performance of the presented scheme compared to a FCS-MPC-based on a state-of-the-art WB motor model using look-up tables for adressing (cross-)saturation.

An Integrated Built-In Test and Repair Approach for Memories with 2D Redundancy

Abstract: An efficient on-chip infrastructure for memory test and repair is crucial to enhance yield and availability of SoCs. Therefore embedded memories are commonly equipped with spare rows and columns (2D redundancy). To avoid the storage of large failure bitmaps needed by classical algorithms for offline repair analysis, existing heuristics for built-in repair analysis (BIRA) either follow very simple search strategies or restrict the search to smaller local bitmaps. Exact BIRA algorithms work with sub analyzers for each possible repair combination. While a parallel implementation suffers from a high hardware overhead, a serial implementation leads to high test times. The integrated built-in test and repair approach proposed in this paper interleaves test and repair analysis and supports an exact solution without failure bitmap. The basic search procedure is combined with an efficient technique to continuously reduce the problem complexity and keep the test and analysis time low.