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High dynamic response has always been one of the goals of the surface-mounted permanent magnet synchronous motor (SPMSM) servo system. To this end, this article proposes an antidisturbance enhanced finite-time composite control strategy for the SPMSM servo system. First, a finite-time controller is designed to enhance the rapidity of the SPMSM speed loop. Second, an antidisturbance enhanced finite-time disturbance observer is proposed, which optimizes the disturbance compensation channel and accelerates the disturbance compensation rate. Meanwhile, this method decouples the parameters of the controller and disturbance observer, making it easier to tune the parameters. Finally, combining the controller with the disturbance observer, the proposed composite control scheme has better dynamic performance than the traditional finite-time composite controller. The experimental evaluation results verify the superiority of the proposed method.

Weak signal features of early bearing faults are interfered by environmental noise, which seriously affects the accuracy of diagnosis results. Moreover, a large amount of data calculation and manual parameter adjustment during model training will affect the timeliness and intelligence of diagnosis results. Aiming at the above problems, an intelligent diagnosis method for motor bearing fault based on music theory knowledge novel generative adversarial networks (MTKGAN) is proposed for the first time. First, the game between generation and discrimination models is used to generate fault samples. The Earth-Mover distance is used to measure the distance between the real and generated distribution. The method generates and enhances weak signal features, and the interference of environmental noise on the signal is effectively solved to improve the accuracy of fault diagnosis. Second, inspired by music theory knowledge, the fault feature affine invariance migration method based on adaptive chord transformation strategy is proposed. The problems of Big Data training and manual parameter adjustment are effectively solved to improve the timeliness of fault diagnosis. Finally, the advantages of MTKGAN in early fault diagnosis of motor bearings are verified by comparing the public dataset and motor bearing fault experiment platform with the existing advanced methods.

Synchronous reluctance motors (SynRM) have a highly nonlinear flux-current mathematical relationship, making it difficult to obtain accurate inductance information. The Kalman-filter-based position estimation method is designed by using a single active back electromotive force (AEMF) model. When inductance parameter mismatch occurs, the estimation performance of the AEMF observer designed by using the single-model resonant Kalman filter (SM-RKF) is more sensitive to inductance parameter deviation as the load increases. To inhibit the effects of inductance parameter mismatch on the SynRM sensorless control system, the position estimation method based on a generalized multiple-model resonant Kalman filter (GMM-RKF) is proposed. First, a new AEMF model of the SynRM is derived at the maximum inductance-oriented coordinate frame. Second, the model set of GMM-RKF is built by RKF1 and RKF2 observers, which are derived from the two AEMF models. The two observers exhibit different parameter sensitivity characteristics at different current vector angle ranges. In comparison with SM-RKF, GMM-RKF has superior robustness against inductance mismatch since it combines the areas in two RKFs where the position estimation is robust to inductance mismatch. The effectiveness of the proposed sensorless control scheme is verified through an experimental prototype.

To address the problem of gear clearance causing the limit cycle oscillation at the positioning end, further deteriorating the positioning accuracy of the servo system, and even causing tooth breakage in severe cases, a backstepping control (BC) method based on shaft torque observer (STO) is proposed in this article for a fully closed-loop gear transmission servo system. First, a gear transmission servo control system model is established, which includes a permanent magnet synchronous motor model, a backlash model, and a gear transmission system model. Second, the description function method is utilized to analyze the limit cycle oscillation mechanism of nonlinear systems. Next, speed loop and current loop BC subsystems are proposed. To avoid the problem of limit cycle oscillation and decreased positioning accuracy caused by backlash nonlinearity in the fully closed-loop gear transmission servo system, a STO is proposed and real-time observation and online compensation of shaft torque are carried out, which effectively eliminates the limit cycle oscillation at positioning end. Ultimately, a 2.0-kW gear transmission servo system is used as a case study, the validity of the proposed BC-STO is tested via experimental results, the limit cycle oscillation is effectively eliminated, and the positioning accuracy is significantly improved.

In the interior permanent magnet synchronous motor drive system, the conventional linear active disturbance rejection controller (LADRC) has coupling problems with tracking and antidisturbance under different operating conditions and various complex disturbance conditions due to the influence of bandwidth. To solve abovementioned problem, a series connection leading correction LADRC (SCLC-LADRC) is proposed in this article. As the core of SCLC-LADRC, the SCLC linear extended state observer (SCLC-LESO) is composed of the total disturbance transfer function of the conventional linear extended state observer and a series connection leading correction network. As a result, the estimation range of the LESO for total disturbance is expanded, and the problem of system oscillation caused by excessive observer bandwidth is also solved. Finally, the validity and availability of the proposed method are proved through theoretical analysis and experiments.

The small capacitor drivers use film capacitors instead of electrolytic capacitors in the dc-link, which will improve the life and reliability of the drivers. However, the dc-link voltage of the small capacitor drivers fluctuates seriously, and the ac component in it causes |(ω+6kωg)/ω|th and |(ω−6kωg)/ω|th harmonics in the extended back electromotive force. These harmonics will increase the estimated position error in the sensorless driver. A harmonic suppression method based on adaptive synchronous rotating frame transformation (ASRFT) is presented in this article to improve the control accuracy of sensorless drivers with small capacitors. First, the motor current harmonics caused by harmonics in the dc-link voltage are analyzed, and the relationship between these harmonics and the estimated sensorless position error was derived. Second, ASRFT and fixed synchronous rotation frame transformations can be used to extract current harmonics in corresponding harmonic coordinate systems. Then, the voltage compensation component generated by the harmonic compensation link will be injected into the driver to suppress the current harmonics extracted in the corresponding coordinate system. Finally, the controller parameters of the proposed method are designed. Using a 2 kW permanent magnet synchronous motor sensorless driver with a small capacitor, the proposed method is demonstrated to be effective.