Comparative analysis of neural and P-I controller for

Nagarajan, V. S.; Balaji, M.; Kamaraj, V.; Seetha, B.

doi:10.1109/icees.2014.6924154

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…In [33], an ANN-based speed controller for a PMSM has been implemented, where the ANN is used to predict the actual machine torque which is then fed to a classical field-oriented current control system with flux observer. 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.…”

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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