Abstract This paper present a new fuzzy iterative learning control design to solve the trajectory tracking problem and performing repetitive tasks for rigid robot manipulators. Several times’ iterations are needed to make the system tracking error converge, especially in the first iteration without experience. In order to solve that problem, fuzzy control and iterative learning control are combined, where fuzzy control is used to tracking trajectory at the first learning period, and the output of fuzzy control is recorded as the initial control inputs of ILC. The new algorithm also adopts gain self-tuning by fuzzy control, in order to improve the convergence rate. Simulations illustrate the effectiveness and convergence of the new algorithm and advantages compared to traditional method.

A diagnostic technique based on independent component analysis (ICA), fast Fourier transform (FFT), and support vector machine (SVM) is suggested for effectively extracting signal features in infrasound signal monitoring. Firstly, ICA is proposed to separate the source signals of mixed infrasound sources. Secondly, FFT is used to obtain the feature vectors of infrasound signals. Finally, SVM is used to classify the extracted feature vectors. The approach integrates the advantages of ICA in signal separation and FFT to extract the feature vectors. An experiment is conducted to verify the benefits of the proposed approach. The experiment results demonstrate that the classification accuracy is above 98.52% and the run time is only 2.1 seconds. Therefore, the proposed strategy is beneficial in enhancing geophysical monitoring performance.

This paper conducts low temperature welding tests on Q460GJC thick plate (60 mm), and based on the basic theory of phase transformation structure evolution, a three-dimensional microstructure evolution analysis method for large welded joints is established, and the analysis of the evolution process of multi-layer and multi-pass weld structure under the low temperature environment of thick plates is completed. The comparison and analysis of test and numerical simulation results are in good agreement, which proves that the welding phase transformation model realizes the digitalization of metallurgical phase transformation in steel structure welding, and optimizes welding process parameters. It is of great significance to improve the quality of welding products and lay a foundation for predicting the performance of welded joints from the micro level.