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In this study, the authors present a control method for the permanent magnet synchronous motor (PMSM) driven X–Y motion system for application in industrial and manufacturing apparatus. Backlash and friction effects in ball‐screw actuated systems are common obstacles to achieve high‐accuracy control. The authors investigate the indirect fuzzy controller to demonstrate the feasibility of reference trajectory tracking and derive an adaptive law in the error state space to deal with parameter uncertainties and external disturbances. The authors adjust the fuzzy parameters online, based on the Lyapunov stability theorem, such that the asymptotic stability of the whole system and error convergence can be guaranteed. In the authors' experiments, this indirect fuzzy control approach compensated for the non‐linear friction and disturbance variation of the biaxial linear stage following the application of four contour shapes, i.e. square, triangular, star, and circular reference contours. Both the authors' simulation and experimental results demonstrate substantial improvement in contour tracking performance in terms of average tracking error and tracking error standard deviation, as well as robustness against platform uncertainties. [ABSTRACT FROM AUTHOR]

In this paper, the autoencoder learning method is proposed for the system diagnosis of chamfering tool equipment. The autoencoder uses unsupervised learning architecture. The training dataset that requires only a positive sample is quite suitable for industrial production lines. The abnormal tool can be diagnosed by comparing the output and input of the autoencoder neural network. The adjustable threshold can effectively improve accuracy. This method can effectively adapt to the current environment when the data contain multiple signals. In the experimental setup, the main diagnostic signal is the current of the motor. The current reflects the torque change when the tool is abnormal. Four-step conversions are developed to process the current signal, including (1) current-to-voltage conversion, (2) analog-digital conversion, (3) downsampling rate, and (4) discrete Fourier transform. The dataset is used to find the best autoencoder parameters by grid search. In training results, the testing accuracy, true positive rate, and precision approach are 87.5%, 83.33%, and 90.91%, respectively. The best model of the autoencoder is evaluated by online testing. The online test means loading the diagnosis model in the production line and evaluating the model. It is shown that the proposed tool can effectively detect abnormal conditions. The online assessment accuracy, true positive rate, and precision are 75%, 90%, and 69.23% in the original threshold, respectively. The accuracy can be up to 90% after adjusting the threshold, and the true positive rate and precision are up to 80% and 100%, respectively. [ABSTRACT FROM AUTHOR]