To improve contour accuracy of two-degree-of-freedom parallel mechanisms for complex high-curvature trajectories under strong coupling, nonlinear friction, and nonrepetitive disturbances, a dual-loop contour error control method combining Active Disturbance Rejection Control (ADRC) and task-space-based iterative learning control (TS-ILC) is proposed. In the inner loop, a joint-space velocity-loop ADRC is designed to estimate and compensate lumped uncertainties, including inertial coupling, friction, and external disturbances, thereby enhancing disturbance rejection and weakening inter-joint coupling. In the outer loop, the shortest normal contour error is estimated in task space using the Newton iterative method and mapped into a joint-space learning error through the inverse Jacobian. A PD-type ILC with zero-phase filtering is then applied for repetitive error compensation. A MATLAB/Simulink and Simscape Multibody co-simulation platform is built to evaluate the method under white noise and random step load disturbances. Results on heart-shaped and five-leaf clover trajectories show that the proposed method achieves good robustness and convergence, reducing the maximum contour error by 74.15% and 54.48%, respectively, compared with the method that combines ADRC with joint-space tracking-error ILC. This study provides an effective solution for complex-trajectory contour control of high-speed, high-precision parallel mechanisms.

To address the problems that vibration signal of flip-chip defect detection in avionics equipment is easily affected by noise, and the defect characteristics are not obvious, a Convolutional Sparse Coding (CSC) method based on locally reweighted is proposed to reconstruct and de-noise. In this study, CSC model is used to globally represent the vibration signal, so as to avoid the problem of high dictionary dimensions and effectively reduce the computational complexity of training dictionary and sparse decomposition. Secondly, to solve the problem of different sparsity of flip-chip vibration signals, a reweighted CSC model is proposed. In order to suppress local noise, a local reweighted CSC model is constructed. In the iterative process, the energy entropy is redistributed in the way of weights, and applied to the weighted strategy, which can better match the local block CSC structure. In addition, an effective acceleration strategy of Stochastic Gradient Descent (SGD) is proposed, which uses Anderson acceleration (AA) extrapolation method to accelerate the SGD algorithm. This strategy linearly combines the historical iteration information of the convolution dictionary to accelerate the learning of the convolution dictionary and improve the accuracy of dictionary solution. The results of simulation and actual flip chip vibration signal experiments show that the proposed CSC method can effectively remove noise in flip chip vibration signal, and is more competitive and superior to the existing popular CSC denoise algorithms.

The vibration signals of rolling bearings are susceptible to strong noise interference. In addition, the lacking of fault samples for rolling bearings increases the difficulty of fault diagnosis. A fault diagnosis model based on conditional generative adversarial network (CGAN) and convolutional denoising auto-encoder (CDAE) is proposed to solve these problems. CGAN is used to generate new samples with the same distribution as the real samples. In order to improve the anti-noise ability of the model, we use CDAE as the discriminator model of CGAN to extract more robust features and achieve more accurate discrimination and classification. The generator and the discriminator are optimized by the adversarial mechanism to improve the quality of sample generation and the accuracy of fault classification. The experimental results show that the CGAN-CDAE model has good anti-noise ability, and achieves good fault diagnosis performance of rolling bearings in the case of small samples and class imbalance.