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This paper investigates the distributed optimization problem for a class of nonlinear uncertain multi-agent systems with unmeasured states, switched parameters, and directed communication topologies changing in the control process. To achieve the goal of optimizing the global objective function, a penalty function is constructed through making up of a sum of local objective functions and integrating consensus conditions of the multi-agent systems to utilize local and neighboring information. Radial basis function neural-networks and neural-networks state observer are applied to approximate the unknown nonlinear functions and obtain the unmeasured states, respectively. To avoid "explosion of complexity" and obtain derivatives for virtual control functions continuously, dynamic surface control technology is proposed to develop a distributed adaptive backstepping neural network control protocol to ensure that all the agents' outputs asymptotically reach consensus to the optimal solution of the global objective function. Simulations demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Industrial Internet-of-Things systems suffer from a vast and vulnerable attack surface, raising widespread concerns about shielding IIoT devices from malicious attacks and reducing cyber risks. Device identification is the prerequisite to safeguard IIoT systems. We leverage the observation that IIoT network protocol implementations vary due to different hardware architectures/configurations and design tasks of IIoT devices, which cause the difference in their network traffic payloads. Specifically, we develop a novel neural network to learn the semantic/syntax features among multiple IIoT packets. The neural network has multiple branches, each of which consists of convolution layers, attention modules, and highway units for learning the classification model of IIoT devices. To validate the precision and recall of our neural network in IIoT devices fingerprinting, we have implemented a prototype of the proposed IIoT device identification system. Our results show that our approach achieves 95.8% precision and 95.4% recall, significantly outperforming other classification models. • We propose a novel IIoT fingerprinting approach based on neural network. • We conduct experiments to validate the effectiveness of it. • The results show that our approach can achieve 95.8% precision. • We demonstrate the usefulness of fingerprinting in application. • Our approach is useful in shielding industrial systems. [ABSTRACT FROM AUTHOR]