Model predictive current controller is a popular and effective technique to provide fast dynamic response in the field of motor control. However, conventional predictive controllers are susceptible to deteriorating control performance when model mismatch exists, such as changes in motor parameters due to the temperature variations. Therefore, this article proposes a precise model-aid extended state observer (MAESO) compensation-based real-time model predictive current controller with enhanced parameter robustness performance and high bandwidth. The predictive controller is converted into the form of multiparameter quadratic programming for online solution using numerical computational method and the constraints are linearized. In addition, the disturbances estimated by MAESO are fed back to the controller in the form of parameters for cycle-by-cycle compensation without extra controller design. Comparative simulations and experiments under different operating conditions are carried out to verify the effectiveness and superiority of the proposed method.

Dual three-phase permanent magnet synchronous motor drives have gained considerable interest in those applications requiring high levels of reliability. However, they often suffer from high-order harmonic currents and mismatch of motor parameters, resulting in degraded performance and reduced efficiency. Therefore, this paper proposes a dual-mode disturbance observer based model predictive current controller. Not only direct current disturbances caused by motor parameters, but also alternating current disturbances caused by high-order harmonic currents are explicitly modeled and estimated to achieve improved model accuracy. In addition, the observer parameter design is given in detail. Furthermore, a generalized predictive current controller with the integration of the proposed observer is developed to provide satisfactory harmonic current suppression capability and parameter robustness simultaneously. Proof of the stability with the proposed controller is given and several experiments and comparisons demonstrate the effectiveness of the proposed method.

Model predictive controllers are widely discussed in the field of dual three-phase permanent magnet synchronous motor control. However, conventional predictive current controllers usually suffer from parameter inaccuracies or model uncertainties, resulting in prediction errors and deterioration of control performance. Therefore, in this paper, an extended state observer-based (ESO) model predictive current controller is proposed to effectively improve the dynamic performance of the motor and its robustness to parameters or disturbances. Parameter inaccuracies or model uncertainties are considered to be lumped disturbances and expressed in the modified mathematical model of the motor. Then, with the designed observer estimating the external disturbances in real time, the prediction error is compensated and corrected periodically. Additionally, the parameter design method of the observer is presented to simplify the controller design. Finally, comparative experiments are implemented to sufficiently demonstrate the effectiveness of the proposed method for dynamic performance improvement as well as for parameter robustness. The results show that the proposed method takes only $17\mathsf{\mu }\mathrm{s}$ of computation time with a closed-loop bandwidth of $1839\mathrm{rad}/\mathrm{s}$. In addition, the maximum d-axis following error of the proposed method is only $0.10\mathrm{A}$ in the load dynamics experiments, which is a significant improvement compared to the $0.79\mathrm{A}$ of the traditional proportional-integral controller.

To determine initial rotor position, permanent magnet synchronous motor drives with incremental encoders typically require motion at startup, which is not acceptable in some practical applications. To address this issue, this article proposes an improved motor parameter-free and fast initial position detection (IPD) strategy with normalized current and adaptive super-twisting sliding-mode observer (ASTO). First, a high-frequency (HF) square-wave voltage is injected to obtain the HF current response in stationary frame. Then, a modified rotor position error extraction method, which does not require information about motor parameters, is applied using the normalized current response. Besides, the ASTO allows the initial rotor position to be rapidly observed. The stability of the observer is analyzed to ensure convergence of the proposed method. Finally, multiple sets of experiments have been carried out to demonstrate the effectiveness and accuracy of the proposed strategy for fast IPD, as well as its robustness against motor parameter uncertainty.

Three-phase voltage source converter (VSC) systems are widely used in the industry. The open-switch faults of VSC systems are not noticeable, but they can result in a major disaster. Aimed at this problem, an open-switch fault diagnosis method using tracking differentiator is proposed. It is found that both single and multiple open-switch faults can cause significant changes in differential current, which can be utilized to diagnose the faults. The tracking differentiator is used to estimate the differential currents without circuit characteristics or additional hardware. With the help of it, both single and multiple open-switch faults can be diagnosed by obvious features, which avoids the comparison of multiple parameters. Furthermore, the proposed method exhibits excellent anti-interference performance as well as high robustness under a variety of conditions. The efficiency of the method was demonstrated by the results of the experiments.