Journal Article•
A virtual laboratory for neural network controlled DC motors based on a DC-DC buck converter
01 Jan 2012-International Journal of Engineering Education (Instituto de Relaciones Internacionales "Daza de Valdes")-Vol. 28, Iss: 3, pp 713-723
TL;DR: In this article, an NNC training set of the DC converter-fed Permanent Magnet Direct Current (PMDC) motor was prepared for the electrical machinery courses, which has a flexible structure and agraphical interface.
Abstract: DC-DC converters have a wide usage as the driver circuit of direct current (DC) motors. This has necessitated sensitive speedcontrols to be made on DC motors. Classical controllers have lower performance due to the non-linear features of DC motors,such as saturation and friction. The Neural Network Controllers (NNC) are widely used in controlling poorly-defined, nonlinearand uncertain systems. NNC courses are now being offered by several universities at the bachelor0s and master’s degree levels as aresult of NNC’s successful applications in these fields. However, the training of an NNC driver circuit in a laboratory environmentis a time-consuming and expensive task. In this study, an NNC training set of the DC converter-fed Permanent Magnet DirectCurrent (PMDC) motor, which is part of the electrical machinery courses, was prepared. The set has a flexible structure and agraphical interface. Thanks to this set, it has become possible to change the PMDC motor and controller parameters, and monitorthe system’s reaction under various operational conditions in graphics. This training set can also guarantee effective learning andcomprehension of Artificial Neural Networks (ANN).
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TL;DR: In this article, a smooth starter based on a dc/dc Buck power converter for the angular velocity trajectory tracking task of a dc permanent magnet motor is presented, which is integrated by a control associated with the dc motor based on differential flatness at the high level, and a control related with the DC/dc buck converter based on cascade control scheme at the low level.
Abstract: In this paper a smooth starter, based on a dc/dc Buck power converter, for the angular velocity trajectory tracking task of a dc permanent magnet motor is presented. To this end, a hierarchical controller is designed, which is integrated by a control associated with the dc motor based on differential flatness at the high level, and a control related with the dc/dc Buck converter based on a cascade control scheme at the low level. The control at the high level allows the dc motor angular velocity to track a desired trajectory and also provides the desired voltage profile that must be tracked by the output voltage of the dc/dc Buck power converter. In order to assure the latter, a cascade control at the low level is designed, considering a sliding mode control for the inner current loop and a proportional-integral control for the outer voltage loop. The hierarchical controller is tested through experiments using MATLAB-Simulink and the DS1104 board from dSPACE. The obtained results show that the desired angular velocity trajectory is well tracked under abrupt variations in the system parameters and that the controller is robust in such operation conditions, confirming the validity of the proposed controller.
157 citations
Cites result from "A virtual laboratory for neural net..."
...In contrast with the previous works, in 2012 Bingöl and Paçaci [12] presented a virtual laboratory, for the angular velocity task, that included a neural network controllers training set for a dc motor powered by a dc/dc Buck converter....
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...In contrast with the previous works, in 2012 Bingöl and Paçaci [12] presented a virtual laboratory, for the angular velocity task, that included a neural network controllers training set for a dc motor powered by a dc/dc Buck converter....
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TL;DR: In this paper, a sensorless control based on the exact tracking error dynamics passive output feedback (ETEDPOF) methodology is proposed for executing the angular velocity trajectory tracking task on the "full-bridge Buck inverter-DC motor" system.
Abstract: A sensorless control based on the exact tracking error dynamics passive output feedback (ETEDPOF) methodology is proposed for executing the angular velocity trajectory tracking task on the “full-bridge Buck inverter–DC motor” system. When such a methodology is applied to the system, the tracking task is achieved by considering only the current sensing and by using some reference trajectories for the system. The reference trajectories are obtained by exploiting the flatness property associated with the mathematical model of the “full-bridge Buck inverter–DC motor” system. Experimental tests are developed for different desired angular velocity trajectories. With the aim of obtaining the experimental results in closed-loop, a “full-bridge Buck inverter–DC motor” prototype, Matlab-Simulink, and a DS1104 board from dSPACE are employed. The experimental results show the effectiveness of the proposed control.
41 citations
TL;DR: In order to solve the trajectory tracking task associated with the bidirectional DC/DC Buck power converter-inverter-DC motor system, a sensorless passivity-based control is presented for the first time using the exact tracking error dynamics passive output feedback (ETEDPOF) methodology.
Abstract: In order to solve the trajectory tracking task associated with the bidirectional DC/DC Buck power converter‑inverter‑DC motor system, a sensorless passivity-based control is presented for the first time. In particular, the exact tracking error dynamics passive output feedback (ETEDPOF) methodology is used. To this end, the required nominal trajectories for the synthesis of the ETEDPOF-based control are defined by means of the system differential flatness property. With the intention of verifying the designed control performance, experimental tests are accomplished through Matlab-Simulink, ControlDesk, and a DS1104 board. The obtained experimental results show a good performance of the ETEDPOF-based control.
28 citations
TL;DR: This paper presents a passivity-based control for the DC/DC Buck-Boost converter–inverter–DC motor system that exploits the energy structure associated with the system error dynamics in order to solve the trajectory tracking task for both the converter voltage and motor bidirectional angular velocity, without using electromechanical sensors.
Abstract: This paper presents a passivity-based control for the DC/DC Buck-Boost converter–inverter–DC motor system. Such control exploits the energy structure associated with the system error dynamics. This in order to solve the trajectory tracking task for both the converter voltage and motor bidirectional angular velocity, without using electromechanical sensors. The successful experimental validation of the proposed control is performed in a built prototype of the system, using Matlab-Simulink and a DS1104 board.
26 citations
TL;DR: A Safe Experimentation Dynamics (SED) algorithm is employed as a data-driven optimization tool to find the optimal PA-PI controller parameters such that the integral square of error and input are reduced.
Abstract: This paper presents a new Piecewise Affine Proportional-Integral (PA-PI) controller for angular velocity tracking of a buck converter generated dc motor. A Safe Experimentation Dynamics (SED) algorithm is employed as a data-driven optimization tool to find the optimal PA-PI controller parameters such that the integral square of error and input are reduced. The essential feature of the PA-PI controller is that the parameters of proportional and integral gains are adaptive to the error variations according to the Piecewise Affine (PA) function. Moreover, the proposed PA function is expected to provide better control accuracy than the other existing variable structure PID controller. In order to verify the effectiveness of the PA-PI controller, a widely known buck converter generated dc motor is considered. The performances of the proposed controller are observed in terms of the integral square of error and input, and the responses of the angular velocity and duty ration input. The simulation results verify that the proposed PA-PI controller yields higher control accuracy than the other existing controllers of buck converter generated dc motor.
26 citations
Cites methods from "A virtual laboratory for neural net..."
...Other control strategies of DC motor fed by DC-DC buck converter are LQR and PI controllers [7], two-stage control with differential flatness [8], H∞ controller [9], robust control law with active disturbance rejection [10], neural network controller [11], and hybrid PI with Fuzzy controller [12]....
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