Novel approach of speed control of PMSM drive using neural network controller

Suman, Shankar; Gautam, Mayank Kumar; Srivastava, Rachit; Giri, V. K.

doi:10.1109/iceeot.2016.7755202

Cited by 20 publications

(6 citation statements)

References 6 publications

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“…In [34], the PI speed controller for a PMSM is also replaced by an ANN-based speed controller, whereas an ANN-based PID speed controller is proposed in [35]. In [36], both, PI speed controller and PI current controllers, are replaced by ANN-based controllers. In general, overshoot as well as settling time were improved by the proposed ANN-based controllers.…”

Section: Artificial Neural Network In Electrical Drivesmentioning

confidence: 99%

“…Lastly, the number of neurons must be defined. A sufficient amount of neurons in the hidden layer(s) to approximate one single (scalar) output varies between three (see [34,36]) and up to 100 (see [47,66]). To specify the number of neurons per hidden layer, the guidelines presented in [47] were adapted to obtain an initial number as starting point or initial guess.…”

Section: Function Name Activation Function φ(X)mentioning

confidence: 99%

“…As artificial neural networks are a promising technology, machine learning-based approaches in electrical drives have already been reported in numerous publications (see the recent overview preprint [30] with 259 references). Exemplary applications of ANNs in the field of electrical drive systems are: ANN-based speed, current or speed and current controllers [31][32][33][34][35][36][37][38], ANN-based parameter/system identification [39][40][41], ANN-based temperature or resistance estimation [42,43], ANN-based direct/predictive torque or model predictive control [44][45][46], ANN-based torque observers [47], ANN-based current waveform prediction [48], ANN-based encoderless control [49][50][51][52], ANN-based torque ripple reduction [53,54], ANN-based condition monitoring or fault detection [55][56][57][58], ANN-based optimal pulse patterns [59], and ANN-based multi-objective optimization for machine design [60].…”

Section: Introduction 1motivationmentioning

confidence: 99%

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Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art

2022

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A novel Artificial Neural Network (ANN) Based Optimal Feedforward Torque Control (OFTC) strategy is proposed which, after proper ANN design, training and validation, allows to analytically compute the optimal reference currents (minimizing copper and iron losses) for Interior Permanent Magnet Synchronous Machines (IPMSMs) with highly operating point dependent nonlinear electric and magnetic characteristics. In contrast to conventional OFTC, which either utilizes large look-up tables (LUTs; with more than three input parameters) or computes the optimal reference currents numerically or analytically but iteratively (due to the necessary online linearization), the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal operation strategy such as e.g., Maximum Torque per Losses (MTPL), Maximum Current (MC) or Field Weakening (FW). Therefore, it is (much) faster and easier to implement while (i) still machine nonlinearities and nonidealities such as e.g., magnetic cross-coupling and saturation and speed-dependent iron losses can be considered and (ii) very accurate optimal reference currents are obtained. Comprehensive simulation results for a real and highly nonlinear IPMSM clearly show these benefits of the proposed ANN-based OFTC approach compared to conventional OFTC strategies using LUT-based, numerical or analytical computation of the reference currents.

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Section: Artificial Neural Network In Electrical Drivesmentioning

confidence: 99%

Section: Function Name Activation Function φ(X)mentioning

confidence: 99%

Section: Introduction 1motivationmentioning

confidence: 99%

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Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art

2022

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“…Als Alternative zu konventionellen Reglern haben sich neuronale Regler etabliert, wie aus Veröffentlichungen zum Thema [3], [4], [5] Dabei werden unterschiedliche Lernmethoden genutzt, wie z.B. das Reinforcement Learning.…”

Section: Introductionunclassified

Simulationsgestützte Auslegung von Reglern mithilfe von Machine Learning

Brown¹,

Strube²

2020

Proceedings ASIM SST 2020

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“…Analysis of the current research in this area shows, that neural controllers are most often used in automation systems of complex nonlinear multidimensional plants operating under conditions of uncertain disturbances, and for which there is no sufficient manual control experience gained by their operators [22][23][24]. This is confirmed by many examples of their successful application presented in a number of works, in particular, in control systems of different types of industrial and mobile robots [25,26], DC and synchronous motors [27,28], power and heating plants [29][30][31], ships [32] and others [33,34].…”

Section: Introductionmentioning

confidence: 99%

Neuroevolutionary Approach to Control of Complex Multicoordinate Interrelated Plants

Kondratenko¹,

Kozlov²,

Gerasin³

2019

IJC

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This paper presents the developed by the authors step-by-step neuroevolution based approach to designing control systems for complex multicoordinate interrelated plants (MIP). The proposed approach allows us to build the structure of the automatic control system (ACS) for the MIP on the basis of the single complex neural controller (NC) with multiple inputs and outputs as well as to implement the effective training of its multilayer neural network (NN) by means of the evolutionary based algorithm, taking into account the mutual influence of all variables of the MIP in an optimal way. In order to study and validate the efficiency of the presented approach the design of the ACS for the spatial motion of caterpillar mobile robot (MR) able to move on inclined and vertical ferromagnetic surfaces is carried out in this work. The developed ACS based on the NC with optimal structure allows us to achieve high quality indicators of spatial motion control, taking into account the mutual influence of control channels of the MR’s speed and angle that confirms the high efficiency of the proposed approach.

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Novel approach of speed control of PMSM drive using neural network controller

Cited by 20 publications

References 6 publications

Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art

Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art

Simulationsgestützte Auslegung von Reglern mithilfe von Machine Learning

Neuroevolutionary Approach to Control of Complex Multicoordinate Interrelated Plants

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