Search

Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors can be used to reduce computation time by using parallel computing. The parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive.

This paper presents a powertrain system for an urban electric vehicle. The powertrain system consists of a hybrid energy source (battery storage and ultracapacitors) and drivetrain system (two in-wheel outer-rotor PMSM motors). Battery performance improvement, has been achieved by supporting it with ultracapacitor energy storage. Power flow control using fuzzy logic controller is presented in detail. An electronic differential algorithms have been implemented and tested.

This paper presents the application of a particle swarm optimization (PSO) to determine iterative learning control (ILC) law gains for an inverter with an LC output filter. Available analytical tuning methods derived for a given type of ILC law are not very straightforward if additional performance requirements of the closed-loop system have to be met. These requirements usually concern the dynamics of a response to a reference signal, the dynamics of a disturbance rejection, the immunity against expected level of system and measurement noise, the robustness to anticipated variations of parameters, etc. An evolutionary optimization approach based on the swarm intelligence is proposed here. It is shown that in the case of the ILC applied to the LC filter, a cost function based on mean squares can produce satisfactory tuning effects. The efficacy of the procedure is illustrated by performing the optimization for various noise levels and various requested dynamics.

This paper discusses benefits of introducing an ultracapacitor (UC) bank into a battery electric vehicle (BEV) powertrain. The case of 12kWh LiFePO4 battery pack is studied quantitatively. Simulation results refer, inter alia, to three main scenarios: fresh cells, half-used battery cells, and half-used ultracapacitors and batteries. Thermal modeling is incorporated into the simulation. Data from real world are considered: various driving cycles recorded using GPS receiver (incl. elevation), discharge curves from battery manufacturer, and UC equivalent series resistance (ESR) variations due to cycling according to real data reported in papers. Cost, as well as gravimetric and volumetric issues are presented. The key decisions referring to an energy storage for BEV being currently designed within the frame of ECO-Mobility Project are highlighted.

In computerized numerical control (CNC) systems, the communication bus between the controller and axis servo drives must offer high bandwidth, noise immunity, and time determinism. More and more CNC systems use real-time Ethernet protocols such as Ethernet Powerlink (EPL). Many modern controllers are closed costly hardware-based solutions. In this paper, the implementation of EPL communication bus in a PC-based CNC system is presented. The CNC system includes a PC, a software CNC controller running under Linux Real-Time Application Interface real-time operating system and servo drives communicating via EPL. The EPL stack was implemented as a real-time kernel module. Due to software-only implementation, this system is a cost-effective solution for a broad range of applications in machine control. All software systems are based on GNU General Public License or Berkeley Software Distribution licenses. Necessary modifications to the EPL stack, Linux configurations, computer basic input/output system, and motherboard configurations were presented. Experimental results of EPL communication cycle jitter on three different PCs were presented. The results confirm good performance of the presented system.

Purpose – The purpose of this paper is to discuss the design and verification of a non‐classical structure of servo‐drive controller with the state feedback and a load torque feedforward compensation.Design/methodology/approach – First a well known nonlinear mathematical model of a PMSM is transformed into a linear form by introducing new variables. The state space new model presented in rotated orthogonal reference frame is decoupled by means of equation in d and q axis. To achieve correct dynamic performance of the servo‐drive system the state feedback with an internal input model and load torque feedforward compensation is proposed. The observed load torque has been used as an input signal for the feedforward compensator. The design of the control system and simulation analysis were performed in Matlab/Simulink. The proposed control algorithm was implemented in a DSP controller (TMS320F2812). The experiments were carried out by using a 0.6 kW PMSM drive system.Findings – It is shown that the proposed c...

The paper presents implementation of PSO (Particle Swarm Optimization) to ANN-based speed controller tuning. Selected learning parameters are optimized according to the control objective function. A battery electric vehicle is considered as a potential plant for an adaptive speed controller. The need for adaptivity in the control algorithm is justified by variations of a total weight of the vehicle. A sizable section of the paper deals with selection of a combined objective function able to effectively evaluate the quality of a solution.

In this paper, auto-tuning procedure of state feedback speed controller for permanent magnet synchronous motor is presented. The novelty of the proposed approach lies in applying nature-inspired artificial bee colony optimization algorithm to obtain weighting matrices that are used for calculation of controller coefficients. In order to maintain selected state and control variables of PMSM in an acceptable level, the optimization algorithm is extended to solve constrained optimization problems. Both the main control objective (i.e. satisfactory dynamic of speed response) and secondary control goals (i.e. zero d-axis current and chattering-free q-axis control signal) are taken into account during auto-tuning process. Proper operation of proposed optimization procedure and employed performance index is proven by experimental tests of the drive.

This paper presents novel constrained state feedback controller designed for SiC MOSFET DC-DC power converter. First, discrete state feedback controller (SFC) with feedforward path and load-current observer is proposed. Next, model predictive approach (MPAC) is used to a posteriori constraint introduction into control system. The proposed control scheme is tested through experiments at switching-frequency of 32 kHz, regarding reference and load variations. It is shown that, thanks to the introduction of model predictive approach constraint into control system, a much better dynamic behavior was obtained, in comparison to non-constrained state feedback control.

This paper describes the particle swarm optimization of the fuzzy logic controller for a hybrid energy storage system in an electric vehicle. The Sugeno-type fuzzy inference system has been applied to divide power between the battery and ultracapacitor energy storage systems, as well as to manage the amount of energy stored in ultracapacitors. The two output signals of the described fuzzy logic controller represent power for each energy storage system, and are the weighted sums of all inference rule outputs. The particle swarm optimization has been proposed to determine the weights of rules. A simulation and experimental validation of the power management algorithm is also introduced in this paper.

Paper presented at MMAR 2016 conference (Międzyzdroje, Poland, 29 Aug.-1 Sept. 2016) Generation of a time-optimal feedrate profile for CNC machines has received significant attention in recent years. Most methods focus on achieving maximum allowable feedrate with constrained axial acceleration and jerk without considering manufacturing precision. Manufacturing precision is often defined as contour error which is the distance between desired and actual toolpaths. This paper presents a method of determining the maximum feedrate for NURBS toolpaths while constraining velocity, acceleration, jerk and contour error. Contour error is predicted during optimization by using an artificial neural-network. Optimization is performed by Particle Swarm Optimization with Augmented Lagrangian constraint handling technique. Results of a time-optimal feedrate profile generated for an example toolpath are presented to illustrate the capabilities of the proposed method.

The paper proposes a significant modification to the plug-in direct multi-swarm repetitive controller (PDMSRC). The recently developed PDMSRC for the sine wave constant-amplitude constant-frequency (CACF) voltage-source inverter (VSI) with an output LC filter serves as a case study. Initially, a purely stochastic control law was employed. Due to the nature of the particle swarm optimization algorithm used to solve the dynamic optimization problem posed by the repetitive control task in real time, the responsiveness of the controller was relatively sluggish. On the other hand, the classic P-type iterative learning control (ILC) law offers fast response. Unfortunately, the classic non-filter-augmented ILC does not offer a practical level of robustness, e.g. a repetitive disturbance can easily destabilize the system, which is not the case in PDMSRC thanks to the appropriate cost function definition. Here a hybridization is proposed to combine the advantages of both approaches. The stochastic control law is replaced with the original part-stochastic part-deterministic swarm movements by incorporating the classic ILC rule into the swarm movements. A convincing improvement in convergence ratio is illustrated with the help of numerical experiments.

The paper discusses selected modifications to a constricted, i.e. canonical, version of the particle swarm optimizer (PSO) enabling effective operation of a repetitive controller. The recently developed plug-in direct multi-swarm repetitive controller (PDMSRC) for the sine wave constant-amplitude constant-frequency (CACF) voltage-source inverter (VSI) with an output LC filter serves as a case study. Online dynamic optimization problems (DOPs), i.e. those solved without switching into some sort of offline/commissioning mode, impose restrictions that are challenging for population-based optimization techniques. In the PDMSRC samples of the control signal are directly stored in the particles. Solutions available for swarms operating in dynamic and noisy environments are briefly discussed and candidates for the PDMSRC are indicated. Geometric versus statistical and cumulative versus dimension-wise diversity measures are tested. The advantages of replacing the frequently used non-dimension-wise repulsion with a dimension-wise mechanism are demonstrated here. The effectiveness of the proposed approach is illustrated with the help of numerical experiments.

This paper presents the adaptive speed control in the permanent synchronous motor (PMSM) drive for a non-stationary repetitive process using particle swarms. The implementation of the particle swarm optimizer (PSO) for the speed controller on-line optimization in PMSM drive system is proposed. The PSO is utilized for the purpose of optimizing in real-time the proportional and integral gains of the speed PI controller for the PMSM drive system with a variable moment of inertia. The variable evaporation factor is introduced, instead of the classical evaporation constant for the PSO. The proposed PSO optimization solution is investigated with the aid of numerical simulation.

POWER ELECTRONICS AND DRIVES; ISSN 2451-0262

Trotz des geringen Anteils an Minderheiten, 94,83 % der Einwohner in Polen bekennen sich zu einer polnischen nationalen Identität, betreibt Polen eine aktive Minderheitenpolitik. Sie dient dem Schutz der nationalen und ethnischen Minderheiten und dem Schutz ihrer Sprachen wie auch der Regionalsprachen. Auf lokaler Ebene besteht unter bestimmten Bedingungen die Möglichkeit, mehrsprachige Beschilderungen und die Minderheitensprachen als Hilfsprachen im Behördenverkehr einzuführen. Für die Bildung der Minderheiten, wie zum Beispiel Unterricht in der »Sprache des Herzens«, werden Mittel aus dem öffentlichen Haushalt bereitgestellt sowie auch für einmalige oder mehrjährige Aktivitäten zum Schutz der Bewahrung und Entwicklung der kulturellen Identität. Die Wahlordnung zum Sejm und zum Senat befreit die von den Organisationen der nationalen Minderheiten aufgestellten Wahllisten von der landesweiten Fünf-Prozent-Hürde. Dennoch verweisen Vertreter der Minderheiten auf die geringe Kenntnis der polnischen Mehrheit über die ethnische Vielfalt des Landes sowie auf die ablehnende Haltung mancher Polen. Als beunruhigend empfanden die Minderheitenvertreter die Entscheidung von Präsident Andrzej Duda, gegen die geplante Novelle des Minderheitsgesetzes ein Veto einzulegen.

This article touches on autochthonic national and ethnic minorities in Europe, not immigrant collectives. The introduction defines "discrimination on the basis of history." Subjective and objective discrimination on the basis of history are differentiated. The categories introduced serve to describe the "memory collective" (a technical term defined in the article) "grandfather in the Wehrmacht," or Polish citizens, whose descendants served—voluntarily or, more frequently, as a result of local pressures or direct coercion—in the army of the Third Reich. In the aftermath of the war, people serving in the Wehrmacht were discriminated against, repressed, and ostracized, and even today their descendants are perceived as people who do not fully belong to the Polish national collective, which is clearly illustrated by the Polish presidential elections of2005, analyzed in the article. The article concludes with a discussion of the results of research on collective memory among the youth at a high school in Krzanowice, whose residents are autochthonic or immigrants and of varying national identification (above all, Polish and German). The conclusion considers the utility of the category of discrimination on the basis of history for describing the situation of ethnic and national minorities.

The paper presents a modified version of a training algorithm for a B-spline network (BSN). The algorithm is used as a controller for a constant-amplitude constant-frequency voltage-source inverter (CACF VSI) with an LC output filter. The modification is divided into three parts. Firstly, there are B-spline functions of higher orders that are used especially functions of a second order. Secondly, the B-spline functions are described in cyclic manner which allows to achieve smooth control signal in steady state. Last modification involves introduction of a shift between the error and the control signal. It improves convergence of the algorithm and allows to achieve a long term stability without using the forgetting factor.

The paper presents a recently developed repetitive neurocontroller (RNC) that does not require additional filtering and/or forgetting to robustify it, i.e. to circumvent the long horizon stability issue present in the classic iterative learning control (ILC) scheme. Initially, the Levenberg-Marquardt (L-M) error backpropagation (BP) algorithm was used as a DOP(dynamic optimization problem)-capable search mechanism. At that time the choice of the training algorithm was made based on the frequently reported effectiveness of the L-M method in static optimization problems. However, there is an abundance of neural network training methods characterized, e.g., by different convergence rates, computational burden, noise sensitivity, etc. The performance of a particular optimization method is always problem specific. The case study of a constant-amplitude constant-frequency (CACF) voltage-source inverter (VSI) with an LC output filter is analysed here and some recommendations regarding the trade-off between convergence rate and computational complexity are made. The robustness to a measurement noise is also tested. The comparison is based on the results of numerical experiments. A couple of algorithms is then suggested for real-time implementation.

In this paper a 9 kW DC/DC converter based on SiC MOSFET devices is presented. Precise control of the output voltage is realized with the help of a state feedback controller with an additional integral action. Simulation and experimental test results as well as an efficiency of DC/DC converter are presented. The behavior of the converter was investigated during no-load and variable load operation. Device efficiency was also investigated and it was found that the efficiency exceed 98% in a wide operating range.

This paper introduces a simulation model of battery-ultracapacitor hybrid energy storage system. The study aims at creating adequate model to investigate the benefits of energy storage system hybridization for an electric vehicle. The experimental tests have been carried out in order to identify the parameters of lithium battery and ultracapacitor. The dynamic models are able to simulate terminal voltage of energy storage including the dependencies on state of charge and temperature.

This paper presents a synthesis of a control system of a three-phase three-level PWM rectifier operated under unbalanced grid voltage conditions. The main goal is to obtain balanced grid currents. The proposed control method contains a PI dc-link voltage controller and an inner state feedback current controller coordinated in synchronous reference frame. The state-space model, which describes the dynamics of an input L-type filter of the rectifier, is augmented by a disturbance model that contains integral and oscillatory terms. Moreover, two approaches of reference tracking are considered. In order to determine the current controller gains, linear-quadratic (LQ) optimization is used. The discussed issues are illustrated by experimental results to validate the accuracy of the proposed control design.

In this paper different randomness scenarios for the recently developed direct particle swarm controller for repetitive processes are investigated and compared. The proposed controller employs the particle swarm optimizer (PSO) to solve in on-line mode the dynamic optimization problem (DOP) designed to shape the control signal in the constant-amplitude constant-frequency (CACF) voltage source inverter (VSI) with an LC output filter. The controller is of a stochastic nature. The DOP at hand is of αD dimensionality, where α denotes the number of control signal samples per each period of the reference voltage signal. Originally, the PSO requires pseudorandom number generators (PRNG) to be run throughout the iterative search to get a new set of numbers in each sample time. In DOP scenarios the swarm has to be kept alive during the operation of the inverter. This in turn implies that the pseudorandom numbers are to be generated in real-time using digital signal controller (DSC) resources. Four different randomization schemes have been tested: the dimension-and-particle-wise one, the dimension-wise one, the particle-wise one and an almost-deterministic one (also known as a list-based PSO). The last approach does not employ any PRNG in real time and as such establishes an appealing alternative in terms of its low computational burden. The effectiveness of such a scheme, when applied to the direct swarm controller, has been studied numerically in the paper.

The object of this article is to explain ethnic and nationality animosities and conflicts at the symbolic level (e.g,. conflicts over monuments, mausoleums, cemeteries, holy sites, language) based on the case of conflict between the German minority and Polish majority in Poland since 1989. Three explanatory paradigms are introduced, economic, cultural, and political, which, taken separately, express some truths about these conflicts but at the same time offer an excessively reductionist vision of social reality. The author therefore proposes to treat ethnic and nationality conflicts as fights about symbolic dominion over a given territory (i.e., fights over symbolic domains) that guarantees access to large stocks of symbolic capital in the sense intended by Pierre Bourdieu—capital active on different social levels. Through the lens of these categories, the author seeks to characterize and explain conflicts over monuments on Polish territory.

Two approaches to speed estimation of an induction motor in the drive system, utilizing only easy measurable electrical signals, are presented, discussed and compared. One is based on phenomenological model of the motor and least squares solution of an overdetermined set of linear equations. Another utilizes nonlinear system modeling via neural network. These two models are complementarily treated in the paper. The phenomenological model is simple and easily interpretable, but it is very sensitive to parameter changes. The neural model requires that input variables are preprocessed. We demonstrate that the input variables selection, sampling time and neural architecture are interrelated and that time instances of sinusoidal signals such as stator voltages and currents, without preprocessing, are not convenient for speed estimation. It is proved that for the most commonly used tapped delay neural architecture, the speed estimation cannot be improved above certain level of accuracy through sampling time selection, or enlarging number of delays of the input signals. Preprocessing of the input variables may change the situation. The information obtained from phenomenological model is used to select and preprocess input variables for the neural model. Simulation examples demonstrating both approaches and very good efficiency and robustness of the neural model are included.