This article focuses on the problem of robust distributed fault estimation for nonlinear multiagent systems with actuator faults and sensor faults. The communication topology of the multiagent systems is assumed to be directed. A novel intermediate observer design method is proposed to estimate the system states, actuator faults, and sensor faults. For the observer constructed in one agent, the output estimation errors of itself and its neighbors are considered, simultaneously. The observer matching condition is not needed in the observer design process. Based on Schur decomposition, the observer parameter calculation method is presented in terms of solution to one linear matrix inequality, which is with the same order as it is for the single agent system. Thus, the calculated amount remains unchanged even when the number of agents increases, since the inequality dimension is independent of the agent number. At last, simulation results are provided to illustrate the effectiveness of the proposed technique.

This paper is devoted to the problem of distributed fault estimation for multiagent systems with nonlinear dynamic. For each agent, an augmented system can be obtained by treating the possible fault as a special system state. Utilizing the output of the neighbor agents, the nonlinear fault estimation observer can be constructed in one agent. The possible faults in both this agent and its neighbors can be estimated by the observer. By solving linear matrix inequalities, the parameter matrices of the observer can be computed. The region pole constraint is considered, which can improve the estimation performance. At last, both a numerical simulation and a network of four one-link flexible joint manipulator systems are considered to verify the proposed fault estimation approach.

Hot-rolled strip steel is widely used in automotive manufacturing, chemical and home appliance industries, and its surface quality has a great impact on the quality of the final product. In the manufacturing process of strip steel, due to the rolling process and many other reasons, the surface of hot rolled strip steel will inevitably produce slag, scratches and other surface defects. These defects not only affect the quality of the product, but may even lead to broken strips in the subsequent process, seriously affecting the continuation of production. Therefore, it is important to study the surface defects of strip steel and identify the types of defects in strip steel. In this paper, a scheme based on ResNet50 with the addition of FcaNet and Convolutional Block Attention Module (CBAM) is proposed for strip defect classification and validated on the X-SDD strip defect dataset. Our solution achieves a classification accuracy of 94.11%, higher than more than a dozen other compared deep learning models. Moreover, to adress the problem of low accuracy of the algorithm in classifying individual defects, we use ensemble learning to optimize. By integrating the original solution with VGG16 and SqueezeNet, the recognition rate of oxide scale of plate system defects improved by 21.05 percentage points, and the overall defect classification accuracy improved to 94.85%.