Huan Wang

The most distinctive feature of synchronous reluctance motor (SynRM) is self- and cross-saturation, which results in a lack of physically consistent guidelines for an accurate analytical model with current as an independent variable. In this paper, an analytical reciprocity magnetic model (RMM) is proposed to describe the nonlinear magnetic behaviors of SynRM. In the proposed model, the physical constraints are obtained by the analysis of the excitation level in the self-axis and the energy conservation relationship between the orthogonal windings. Then, based on the irrotationality of flux linkage in the coupling field, a modified Gaussian function is constructed to describe the saturation and magnetic co-energy. Furthermore, the advantages of the proposed model are verified through the proposed dual-axis hybrid excited self-commissioning method and the two-stage collaborative coupling algorithm. In comparison with other modeling techniques, the proposed RMM shows superior comprehensive performance. The experimental results demonstrate that the proposed model owns high accuracy and availability.

The performance of synchronous reluctance motor (SynRM) is strongly dependent on the current loop dynamic characteristics. However, existing drawbacks in simplifying the nonlinear behaviors restrict the complete decoupling of the dynamics, inducing current overshoot and oscillation. In this article, a cascade hybrid decoupling control scheme is proposed to mitigate the negative effects of magnetic circuit nonlinearity (MCN) on current dynamics. A nonlinear model is first established to describe the current dynamics with considerations of MCN. Then, the coupling mechanism between dq -axis current dynamics is classified and analyzed. The dynamic decoupling condition is derived involving both back-EMF and cross-induced electromotive force. Furthermore, an adaptive backstepping current controller is presented to cope with changes in operating point caused by self-saturation. Uncertainties caused by model errors and unmeasurable disturbances are adaptively compensated. Comprehensive experimental results demonstrate that the proposed scheme could reduce torque and current ripple while improving response performance.

The synchronous reluctance machine (SynRM) has gained increasing attraction for future electric drive systems. The characterization of its magnetic and current dynamics is essential for high-performance model-driven control, which still remains a promising problem. In this article, a dynamic characterization scheme for SynRM with dual axis hybrid excitation self-comissioning is proposed to characterize the magnetic and current dynamics. Inspired by separating dual-axis current variables, a compact magnetic circuit model is investigated with reciprocity analytics. Furthermore, an improved current dynamic model is derived with resulting analytical apparent and incremental inductances. On this basis, the characterized parameters involved in the magnetic circuit and current dynamic models can be accurately identified by combining the designed dual axis hybrid excitation method with effective parameter identification algorithm. The effectiveness and practicability of the proposed scheme are verified based on the development platform for commercial SynRM drive. This simple dynamic characterization scheme fits for online implementation on standard drives. Compared with the existed work, 38.2 ${\%}$ improvement of normalized modeling accuracy for magnetic circuit can be achieved. In the meanwhile, the normalized modeling errors of apparent inductances for current dynamics are strictly less than 6.91 ${\%}$ .

The speed profile tracking calculation of high-speed maglev trains is mainly affected by running resistance. In order to reduce the adverse effects and improve tracking accuracy, this paper presents a maglev train operation control method based on a fractional-order sliding mode adaptive and diagonal recurrent neural network (FSMA-DRNN). First, the kinematic resistance equation is established due to the three types of resistance that occur during the actual operation of a train: air resistance, guide eddy current resistance, and suspension frame generator coil resistance. Then, the FSMA-DRNN control law and parameter update law are designed, and a FSMA-DRNN operation controller is composed of three parts: speed feed forward, fractional-order sliding mode adaptive equivalent control, and diagonal recurrent neural network resistance compensation. Furthermore, by using the designed operation controller, it is proven effective by the Lyapunov theory for the stability of the closed-loop control system. Apart from the proposed theoretical analysis, the proposed approaches are verified by experiments on the high-speed maglev hardware-in-the-loop simulation platform Rt-Lab, in line with the 29.86 km test line and a five-car train from the Shanghai maglev, showing the effectiveness and superiority for operation optimization.