The dual-motor drive is common in many heavy-duty swinging arm servo systems. A well-tuned dual-motor drive can provide better stability and durability than the regular single motor servo. However, the backlash is usually unavoidable in the heavy transmission chains. In this article, the issues provoked by the backlash are resolved by a virtual average motor model and a simple anti-backlash torsion torque. The transient performance is guaranteed by the dual-motor synchronous drive, and the anti-backlash strategy is only enabled when the load is approaching the reference position. The challenge is that the motion system structure is transforming during this transition. The model predictive control combined with the extended state observer is applied to get adaptive in this scenario. The expected effect is that the servo can track the desired position with high precision and high response. The scheme is comprehensively validated and evaluated in an experimental servo setup with a dual-motor drive.

This article presents a sensorless machine drive scheme, combining the motion model predictive control (MPC) in the speed loop and the position estimation in the current loop. The current-loop-based position estimation algorithm is to estimate the present rotor position with the phase current information of the previous samples. These methods cannot perform perfectly in the low speed when a heavy load suddenly occurs, as the aggressive motion disturbance can impact the convergence of the low-speed position estimation, or even make divergence and estimation failure. This issue is especially terrible for the surface permanent magnet synchronous machines (SPMSMs), as the magnetic saliency is weak at heavy loads. The target of this article is to solve this issue with respect to the servo system, where the machines need to frequently start and stop with heavy loads, and hence the above risk must be considered. The convex optimization is applied in the position estimation, and the optimization cost value is extended to adjust the injection amplitude and the reference trajectory of the MPC control. With this strategy, the sensorless control can operate with a high stability and accuracy. The scheme is validated in an SPMSM test bench.

Inverter open-circuit faults can cause significant changes in the output current of permanent magnet synchronous motor (PMSM), so many fault diagnosis methods based on convolutional neural networks take the current as monitor signals. However, there are some shortcomings in the application of convolutional neural networks for fault diagnosis. On the one hand, most of the traditional pooling methods confuse the different frequency components of the feature map due to ignoring the classical sampling theorem, which decreases the accuracy of fault diagnosis. On the other hand, the generalization performance of the diagnostic model is not good enough when the data distribution changes due to variations in the operating conditions of the drive system. To address these issues, this paper proposes an open-circuit fault diagnosis method using an adaptive sparse attention wavelet network (ASAWN), where a novel pooling layer, termed as adaptive sparse attention wavelet pooling (ASAW-pooling) layer, is designed using discrete wavelet transform and adaptive sparse attention mechanism. The pooling layer improves the robustness of the diagnostic model by filtering out the high-frequency components of the current data and the redundant information of the attention mechanism. Experimental results show that the proposed method can effectively diagnose 22 types of open-circuit faults. Besides, a comparison with other models shows that the proposed model not only has high accuracy but also performs strong robustness.