Because of its excellent efficiency, compact dimen-sions, and accurate control features, Permanent Magnet Syn-chronous Motor (PMSM) are experiencing widespread applications across various industries. By accurately characterizing the dynamic behavior of PMSM systems through system identi-fication, engineers can ensure that PMSM motors reach their maximum potentials and meet the stringent requirements of modern industrial and technical systems while reducing energy consumption and maintenance costs. The traditional recursive least squares method is sensitive to noises, and unable to accu-rately identify parameters in complex environments. Pure data-driven models lack interpretability and require complex model architecture and computational costs. To this end, this work draws knowledge-informed neural ordinary differential equations (NODEs) for system identification, which embeds system prior knowledge into the NODEs for more efficient and accurate model learning. Comparative simulations show that this method not only obtains a higher-precision system model, but also significantly reduces the amount of training data and computation costs.

With the continuous development of the wind power industry, variable-speed wind turbines (VSWT) are moving towards large-scale, and their applications have been expanded to offshore areas. However, the operating environment of offshore wind farm is even more harsh, and the potential torsional vibration of the drivetrain caused by disturbances in the offshore wind farm may introduce fatigue damage to the VSWT, resulting in unexpected faults that may lead to prolonged shutdown of the VSWT and loss of power generation efficiency. This study proposes a torsional vibration suppression strategy based on unknown input observer and sliding mode control. The proposed suppression strategy provides an appropriate electromagnetic torque compensation for the generator to mitigate disturbances in the whole control system. The effectiveness of the proposed strategy is validated by extensive simulation studies.

Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has attracted great attention due to its high reliability and high-power output capacities. However, the conventional single-voltage-vector-based predictive current control (SV-PCC) for DTP-PMSM presents high torque ripple and current harmonics, and high computational burden. To solve those issues, a modulated-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM is proposed in this paper. Wherein, twenty-four VVs are synthesized by the inherent voltage vectors, and two VVs and one zero voltage vector with optimal duty cycles are determined and applied in each sampling period to improve the steady-state performance. The selection of optimal VVs and the calculation of the optimal duty cycles are simplified by integrating the deadbeat control and modulation scheme. Various comparisons are carried out to validate the effectiveness and superiority of the proposed MVV-PCC strategy.

This article investigates the problem of event-triggered distributed model predictive control (DMPC) for continuous-time nonlinear multiagent systems (MASs) subject to bounded disturbances, input and communication delays simultaneously. The compensation schemes for input and communication delays are proposed, respectively. A new optimal control problem (OCP) to achieve consensus among all agents is formulated under the proposed delay compensation schemes. To alleviate the communication burden and sensing cost, a novel input delay-related periodic event-triggering condition is proposed based on the state error to determine when to calculate the new control input. Sufficient condition guaranteeing the feasibility of the OCP is achieved with the proposed event-triggered control scheme. In stability analysis, a novel time-varying Lyapunov function is constructed, and the input-to-state practical stability of the MASs is derived by using the quasi-Lipschitz continuous property of the Lyapunov function. Numerical results related to vehicle platooning demonstrate the effectiveness of the proposed event-triggered DMPC.

To mitigate the influence of parameter variations on the maximum torque per ampere (MTPA) operation of interior permanent magnet synchronous machines (IPMSMs), a parameter-estimation-based adaptive MTPA control method is presented in this paper. A parameter estimator that exploits the recursive extended least squares (RELS) algorithm is established to accurately estimate the q - and d-axes inductances and the permanent magnet flux linkage of the machine. The optimal current vector angle that is critical in the MTPA operation of the machine can be accordingly calibrated under parameter variation conditions. This paper considers and simulates a speed servo control system where the proposed adaptive MTPA control with RELS algorithm is demonstrated. Extensive case studies have been carried out to verify the effectiveness and robustness of the proposed strategy.

Due to the nonstationary characteristic of the electric locomotives in electric traction systems, the equivalent grid impedance changes frequently, which may lead to detrimental effect on the control system of electric traction PWM rectifiers. The mathematical model of an electric traction PWM rectifier is established, and the double-loop control system is designed. An online grid inductance estimation method for the electric traction PWM rectifier based on dual-frequency harmonic current injection is accordingly proposed. 400Hz and 500Hz are selected as the frequencies of the harmonics injected into the system. Moving window fast Fourier transform (FFT) is performed to extract the amplitudes corresponding to the harmonics. The estimation values of the grid inductance can be obtained from a series of arithmetical operations. The experimental platform for the electric traction PWM rectifier is set up. Simulation and experimental results are demonstrated to verify the effectiveness of the proposed strategy.

This paper investigates the neutral-point (NP) potential unbalance of the fault tolerant topology of three-level neutral-point-clamped (NPC) inverter, namely the eight-switch three-phase inverter (ESTPI). It is illustrated in this paper that the conventional carrier-based pulse width modulation (CBPWM) will cause the NP potential unbalance of ESTPI. Moreover, the NP potential unbalance will lead to the output voltage unbalance, which seriously deteriorates the system performance of ESTPI. As a consequence, an improved CBPWM, where the upper and lower triangular carriers have different amplitudes, is proposed to guarantee a three-phase balanced output voltage of ESTPI. The simulation results demonstrate that the proposed scheme can suppress the unbalance of three-phase output voltage effectively.