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In this study, a generalized torque estimation method is proposed for synchronous motors, including surface permanent magnet synchronous motors (SPMSMs), synchronous reluctance motors (SynRMs), and interior permanent magnet synchronous motors (IPMSMs) for building the analytical motor model. The average motor torque is estimated using the Lorentz force by the generated flux density in the air gap to determine the relationships among torque, flux density, and injected current. In the proposed method, the generated flux density is derived step by step by considering the effects of magnetic flux saturation, the stator slot, the rotor barrier, and permanent magnets (PMs) to ensure that the generated average torque complies with the operating condition of the motor. To verify the proposed method, the output torque of finite element analysis (FEA), Maxwell 2D, is compared to the proposed method in a SPMSM. Moreover, a phasor diagram is plotted to determine the mechanism through which torque is generated in SynRMs and IPMSMs. A SynRM and an IPMSM with ferrites PMs are analyzed using the proposed method, FEA, and the experimental results of this study indicate the effectiveness. [ABSTRACT FROM AUTHOR]

An intelligent servo drive system for a permanent magnet-assisted synchronous reluctance motor (PMASynRM) that can adapt to the control requirements considering the motor's nonlinear and time-varying natures is developed in this study. A recurrent wavelet fuzzy neural network (RWFNN) with intelligent backstepping control is proposed to achieve this. In this study, first, a maximum torque per ampere (MTPA) controlled PMASynRM servo drive is introduced. A lookup table (LUT) is created, which is based on finite element analysis (FEA) results by using ANSYS Maxwell-2D dynamic model to determine the current angle command of the MTPA. Next, a backstepping control (BSC) system is created to accurately follow the desired position in the PMASynRM servo drive system while maintaining robust control characteristics. However, designing an efficient BSC for practical applications becomes challenging due to the lack of prior uncertainty information. To overcome this challenge, this study introduces an RWFNN as an approximation for the BSC, aiming to alleviate the limitations of the traditional BSC approach. An enhanced adaptive compensator is also incorporated into the RWFNN to handle potential approximation errors effectively. In addition, to ensure the stability of the RWFNN, the Lyapunov stability method is employed to develop online learning algorithms for the RWFNN and to guarantee its asymptotic stability. The proposed intelligent backstepping control with recurrent wavelet fuzzy neural network (IBSCRWFNN) demonstrates remarkable effectiveness and robustness in controlling the PMASynRM servo drive, as evidenced by the experimental results. [ABSTRACT FROM AUTHOR]

An intelligent wind power smoothing control using fuzzy neural network (FNN) is proposed in this study. First, the modeling of wind power generator and the designed battery energy storage system (BESS) are introduced. The BESS is consisted of a bidirectional interleaved DC/DC converter and a 3-arm 3-level inverter. Then, the network structure of the FNN and its online learning algorithms are described in detail. Moreover, actual wind data is adopted as the input to the designed wind power generator model. Furthermore, the three-phase output currents of the wind power generator are converted to dq-axis current components. The resulted q-axis current is the input of the FNN power smoothing control and the output is a gentle wind power curve to achieve the effect of wind power smoothing. The difference of the actual wind power and smoothed power is supplied by the BESS. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the FNN power smoothing control. In the experimentation, a digital signal processor (DSP) based BESS is built using two TMS320F28335. From the experimental results of various wind variation sceneries, the effectiveness of the proposed intelligent wind power smoothing control is verified.