This paper studies the output synchronization problem for a class of networked non-linear multi-agent systems with switching topology and time-varying delays. To synchronize the outputs, a leader is introduced whose connectivity to the followers varies with time, and a novel data-driven consensus protocol based on model free adaptive control is proposed, where the reference input of each follower is designed to be the time-varying average of the neighboring agents’ outputs. Both the case when the leader is with a prescribed reference input and the case otherwise are considered. The proposed protocol allows for time-varying delays, switching topology, and does not use the agent structure or the dynamics information implicitly or explicitly. Sufficient conditions are derived to guarantee the closed-loop stability, and conditions for consensus convergence are obtained, where only a joint spanning tree is required. Numerical simulations and practical experiments are conducted to demonstrate the effectiveness of the proposed protocol. 相似文献
Cyber-physical systems integrate computing, network and physical environments to make the systems more efficient and cooperative, and have important and extensive application prospects, such as the Internet of things. This paper studies the control problem of nonlinear cyber-physical systems with unknown dynamics and communication delays. A networked learning predictive control scheme is proposed for unknown nonlinear cyber-physical systems. This scheme recursively learns unknown system dynamics, actively compensates for communication delays and accurately tracks a desired reference. Learning multi-step predictors are presented to predict various step ahead outputs of the unknown nonlinear cyber-physical systems. The optimal design of controllers minimises a performance cost function which measures the tracking error predictions and control input increment predictions. The system analysis leads to the stability criteria of closed-loop nonlinear cyber-physical systems employing the networked learning predictive control scheme. An example illustrates the outcomes of the proposed scheme.
Mammalian Toll-like receptors (TLRs) 3, 7, 8 and 9 initiate immune responses to infection by recognizing microbial nucleic acids; however, these responses come at the cost of potential autoimmunity owing to inappropriate recognition of self nucleic acids. The localization of TLR9 and TLR7 to intracellular compartments seems to have a role in facilitating responses to viral nucleic acids while maintaining tolerance to self nucleic acids, yet the cell biology regulating the transport and localization of these receptors remains poorly understood. Here we define the route by which TLR9 and TLR7 exit the endoplasmic reticulum and travel to endolysosomes in mouse macrophages and dendritic cells. The ectodomains of TLR9 and TLR7 are cleaved in the endolysosome, such that no full-length protein is detectable in the compartment where ligand is recognized. Notably, although both the full-length and cleaved forms of TLR9 are capable of binding ligand, only the processed form recruits MyD88 on activation, indicating that this truncated receptor, rather than the full-length form, is functional. Furthermore, conditions that prevent receptor proteolysis, including forced TLR9 surface localization, render the receptor non-functional. We propose that ectodomain cleavage represents a strategy to restrict receptor activation to endolysosomal compartments and prevent TLRs from responding to self nucleic acids. 相似文献
Journal of Systems Science and Complexity - This paper investigates the attitude and orbit control for the combined spacecraft formed after a target spacecraft without the autonomous control... 相似文献