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This paper develops a novel model free-predictive flux vector control method (MF-PFVC) for high-performance stator flux and torque monitoring of the multi-module three-phase PMSM (N ∗ 3-Phase PMSM) drive with parameters mismatch. The proposed MF-PFVC method is based on a convenient combination of direct predictive torque control (PTC) and model free predictive control techniques. First, the voltage source inverter (VSI) vector control of N ∗ 3-Phase PMSM is determined by the three-segment stator windings structure. Then, the parameter mismatch sensitivity of the N ∗ 3-Phase PMSM drive system is analyzed based on the conventional PTC. Furthermore, a novel MF-PFVC method with multivariable unknown disturbance integral terminal sliding mode observer (SMO) is proposed for N ∗ 3-Phase PMSM drive, which can effectively enhance robustness against the parameters mismatch. Finally, compared with the conventional PTC, the simulation and experimental results of MF-PFVC method illustrated improved stator flux control performance concerning lower stator flux ripple as well as reduced torque ripple while it can eliminate the influence of parameter mismatch.

In this study, a novel robust deadbeat predictive current control algorithm using voltage coefficient matrix update (DPCC-VCMU) is proposed for permanent magnet synchronous motor (PMSM), which can eliminate the influence of model parameter mismatch. First, the parameter sensitivity of conventional deadbeat predictive current control (DPCC) is discussed, and the multiple parameter error online identifiers are designed based on an improved Adaline neural network (ANN) weight adjustment algorithm. The novel identification structure with parameter mismatch error as the weight of neural network is then proposed to enhance the current tracking robustness under model parameter mismatch. Moreover, the parameter error identification value is used to update the coefficient matrix of the control voltage and predict the current value of next moment simultaneously. Finally, compared with conventional DPCC method, the simulation and experimental results verify that the proposed DPCC-VCMU-algorithm can significantly improve the current response accuracy by using voltage coefficient matrix update, while achieving lower torque ripple and smaller current harmonics.

In order to reduce the influence of environmental noise and different operating conditions on the accuracy of motor fault diagnosis, this paper proposes a capsule network method combining multi-channel signals and the efficient channel attention (ECA) mechanism, sampling the data from multiple sensors and visualizing the one-dimensional time-frequency domain as a two-dimensional symmetric dot pattern (SDP) image, then fusing the multi-channel image data and extracting the image using a capsule network combining the ECA attention mechanism features to match eight different fault types for fault classification. In order to guarantee the universality of the suggested model, data from Case Western Reserve University (CWRU) is used for validation. The suggested multi-channel signal fusion ECA attention capsule network (MSF-ECA-CapsNet) model fault identification accuracy may reach 99.21%, according to the experimental findings, which is higher than the traditional method. Meanwhile, the method of multi-sensor data fusion and the use of the ECA attention mechanism make the diagnosis accuracy much higher.

Over the years, many ellipse detection algorithms have been studied broadly, while the critical problem of accurately and effectively detecting ellipses in the real-world using robots remains a challenge. In this paper, we proposed a valuable real-time robot-oriented detector and simple tracking algorithm for ellipses. This method uses low-cost RGB cameras for conversion into HSV space to obtain reddish regions of interest (RROIs) contours, effective arc selection and grouping strategies, and the candidate ellipses selection procedures that eliminate invalid edges and clustering functions. Extensive experiments are conducted to adjust and verify the method’s parameters for achieving the best performance. The method combined with a simple tracking algorithm executes only approximately 30 ms on a video frame in most cases. The results show that the proposed method had high-quality performance (precision, recall, F-Measure scores) and the least execution time compared with the existing nine most advanced methods on three public actual application datasets. Our method could detect elliptical markers in real-time in practical applications, detect ellipses adaptively under natural light, well detect severely blocked and specular reflection ellipses when the elliptical object was far from or close to the robot. The average detection frequency can meet the real-time requirements (>10 Hz).