1,721,030 research outputs found
Inferential processor based fuzzy control for an electric drive
No full textThe paper is aimed to illustrate the design and the implementation of Fuzzy Logic Controllers (FLCs) for electrical drives on the inferential processor OMRON FP3000. The fundamentals of the FLCs are preliminarily illustrated. A description of the inferential processor and a discussion about its capabilities and limitations for this field of applications is first given. Then, the design and the implementation of two different FLCs for the speed and current control of a DC motor drive is given. Experimental results are also included in the paper
A PI consensus controller with gossip communication for clock synchronization in wireless sensors networks
No full textHigh performance PMSM sensorless drive based on stochastic filtering
Full text linkThis paper describes the prototype realisation of a high-performance sensorless permanent magnet synchronous motor (PMSM) drive. Experimental setup, hardware circuitry and software implementation are described into details; particular emphasis is given to the software control algorithms specifically studied and implemented to increase the overall system performance
Novel control technique for high-performance diesel-driven AC generator-sets
No full textThe availability of low-cost high-speed microprocessor has prompted a great interest towards full-digital high performance AC generator sets. This paper deals with the novel application of a multi-resonant amplifier instead of a conventional PI regulator in the voltage closed control loop of a diesel-driven generator. An inner current control loop is implemented to provide limitations of the inductor current in case of short circuit and overload. The experimental results show that the proposed converter gives the system superior performance in terms of both output voltage accuracy and stability, in spite of any load variation
An Effective Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives
Full text linkThe performances of a model predictive control algorithm largely depend on the knowledge of the system model. A model-free predictive control approach skips all the effects of parameters variations or mismatches, as well as of model nonlinearity and uncertainties. A finite-set model-free current predictive control is proposed in this paper. The current variations predictions induced by the eight base inverter voltage vectors are estimated by means of the previous measurements stored into lookup tables. To keep the current variations information up to date, the three current measurements due to the three most recent feeding voltages are combined together to reconstruct all the others. The reconstruction is performed by taking advantage of the relationships between the three different base voltage vectors involved in the process. In particular, 210 possible combinations of three-state voltage vectors can be found, but they can be gathered together in six different groups. A light and computationally fast algorithm for the group identification is proposed in this paper. Finally, the current reconstruction for the prediction of future steps is thoroughly analyzed. A compensation of the motor rotation effect on the input voltages is proposed, too. The control scheme is evaluated by means of both simulation and experimental evidences on two different synchronous reluctance motors
Data-driven predictive current control for synchronous motor drives
No full textData-driven control techniques have become increasingly popular in recent years due to the availability of massive amounts of data and several advances in data science. These control design methods bypass the system identification step and directly exploit collected data to construct the controller. In this paper, we investigate the application of data-driven methods to the control of electric motor drives, and specifically to the design of current controllers for three-phase synchronous permanent magnet motor drives. Two of the most promising data-driven algorithms are presented, namely the Subspace Predictive Control algorithm and the Data-Enabled Predictive Control algorithm. The theory behind these techniques is first reviewed in the optimization-based control framework. Standard algorithms are slightly modified to fulfill the requirements of the specific application, and then simulated in the MATLAB Simulink environment. Some key aspects of real-time implementation are studied, providing a proof-of-concept demonstration of the applicability of these algorithms. The data-driven design is proposed for three different topologies of synchronous motors, proving the flexibility of the approach
On-Line Tracking of the MTPA Trajectory in IPM Motors Via Active Power Measurement
No full textThe real-time tracking of the Maximum-Torque-Per-Ampere (MTPA) trajectory in Interior-Permanent-Magnet (IPM) motors is addressed in this paper. The considered approach is based on the injection of proper current test signals and aims at minimizing torque oscillations at the injected frequency, this last condition assuring MTPA operations. Differently from previous approaches motor active power is monitored to detect the out-of-MTPA condition (i.e. torque oscillations), instead of measured motor speed, thus avoiding the use of high-resolution speed transducers, as required by previous approaches. Analytical development are provided, as well as simulation and experimental results, proving the effectiveness of the proposal
Computation of Self-Sensing Capabilities of Synchronous Machines for Rotating High Frequency Voltage Injection Sensorless Control
No full textPerformance achievable in sensorless control of electrical drives strictly depends on the adopted synchronous machine. The combination of cross-saturation and saliency, both dependent by the current load and the rotor position, makes always the position estimation afflicted by an estimation error. When the machine is highly saturated, the sensorless control can even diverge resulting in a useless drive. Thus, it is of primary importance to know in advance the convergence region of the sensorless drive, i.e., the operating points, where the motor can be successfully controlled without a position sensor. The aim of this article is to show how to compute the estimation error, the saliency, and the convergence region (hereafter called self-sensing capabilities) starting from the flux linkages maps or, more generally, from the incremental inductances. The dependence on the rotor position is also considered. Finite element simulations and experimental measurements validate the proposed model. The code used to compute the self-sensing capabilities is available as an open source package
Sensorless control of Interior Permanent Magnet motor using a Moving Horizon Estimator based on a linearized motor model
No full textThis paper presents a sensorless control solution for Interior Permanent Magnet synchronous motor using a Moving Horizon Estimator. The active flux concept is used to describe the motor model. The position estimation problem is non linear for the considered type of synchronous machine, thus a linearization of the model is performed using a Taylor first order approximation. Thanks to the linearization, the problem assumes the form of an equality constrained Quadratic Programming that can be solved directly, without using any iterative method. The estimator is real-time implemented, coupled with a standard PI speed controller and a Model Predictive Control for the current loop, highlighting the feasibility of this innovative control architecture. Several steady state and dynamic tests are provided in this work to show the drive performances. In details, steady state operation, torque step, speed inversion and a speed ramp are included in the study
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