Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Stochastic adaptive backstepping controller design by introducing dynamic signal and changing supply function

Abstract: A more general class of stochastic non-linear systems with unmodelled dynamics and uncertain non-linear functions are considered in this paper. With the concept of ISpS being extended to stochastic case, by combining changing supply function technique with backstepping design technique, an adaptive output-feedback controller is proposed. It is shown that all the solutions of the closed-loop system are uniformly bounded in probability, and the output can be regulated to an arbitrarily small neighbourhood of the origin in probability. A simulation example demonstrates the control scheme.

Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network

Abstract: Defect detection is an essential requirement for quality control in the production of printed circuit boards (PCBs) manufacturing. The traditional defect detection methods have various drawbacks, such as strongly depending on a carefully designed template, highly computational cost, and noise-susceptibility, which pose a significant challenge in a production environment. In this paper, a deep learning-based image detection method for PCB defect detection is proposed. This method builds a new network based on Faster RCNN. We use a ResNet50 with Feature Pyramid Networks as the backbone for feature extraction, to better detect small defects on the PCB. Secondly, we use GARPN to predict more accurate anchors and merge the residual units of ShuffleNetV2. The experimental results show that this method is more suitable for use in production than other PCB defect detection methods. We have also tested in other PCB defects dataset, and experiments have shown that this method is equally valid.

Optimal Output Regulation for Heterogeneous Multiagent Systems via Adaptive Dynamic Programming

Abstract: In this paper, the optimal output regulation problem for partially model-free heterogeneous linear multiagent systems with disturbance generated by an exosystem is addressed by using adaptive dynamic programming and double compensator method. The topology graph for the information exchange of the agents has a spanning tree. The dynamic of individual agent is assumed to be nonidentical and of different dimensions. One distributed compensator is designed to deal with the nonidentical agents, and the other compensator is used to handle the optimal performance index. By constructing the double compensator, the distributed feedback control laws are designed to make the output of each agent synchronize with the reference output and minimize the energy of the output error simultaneously. To overcome the lack of the dynamics knowledge of each agent, a novel online policy iteration algorithm is developed to obtain the optimal feedback gain matrix. Finally, two examples are presented to illustrate the effectiveness of our results.