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数据融合、强化学习、多智能体系统、优化、事件触发控制、卷积神经网络、非线性系统、卡尔曼滤波...
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美国University of North Texas、Duke University;英国Imperial College London;意大利University of Calabria、University of Rome Tor Vergata;澳大利亚Swinburne University of Technology;德国Humboldt University of Berlin、Potsdam Institute for Climate Impact Research;加拿大University of Windsor;瑞典KTH Royal Institute of Technology;荷兰Delft University of Technology;清华大学、北京航空航天大学、东北大学、武汉大学、华东理工大学...
◆ Research on crowdsourcing learning in the recent fifteen years is reviewed.
◆ A novel knowledge learning framework that includes three dimensions is proposed.
◆ Future research topics under the systematic perspective are discussed.
◆ For cooperative optimization, we focus on the recent work of distributed online optimization and the research of distributed online optimization and federated optimization in privacy protection. ◆ For games, we focus on cooperative games and non-cooperative games from the perspective of static games and dynamic games, respectively. ◆ Bridge the transition from cooperative optimization to games, i.e., cooperative games. ◆ A comprehensive survey is provided to identify current concerns, technologies and future research for cyber attacks on cyber physical systems from the perspective of control theory. ◆ Current studies on availability, integrity and confidentiality attacks are analyzed based on time-driven and event-driven systems. ◆ Comparisons among various studies are provided. ◆ Cooperative enclosing in an ellipsoidal ring of a moving target with uncertain information. ◆ Formation in a containment region with forbidden region constraints. ◆ Double integrator-based swarm model in any finite space dimension. ◆ Proposes a novel distributed SGD algorithm, suitable for arbitrarily connected communication networks and heterogeneous local cost functions. ◆ Proposed algorithm achieves the linear speedup rate for smooth nonconvex cost functions. ◆ Achieves the linear speedup convergence rate when the global cost function satisfies the Polyak–Łojasiewicz condition which is weaker than the commonly used strong convexity assumption. ◆ Attitude regulation problem for a class of satellites is studied. ◆ Persistent disturbances with bounded windowed norms are considered. ◆ Closed-loop system input is saturated. ◆ Trajectories of closed-loop systems are ultimately bounded. ◆ Addresses the bipartite time-varying output formation tracking problem for heterogeneous MASs with multiple leaders and switching communication networks via a novel reduced-dimensional observer-based fully distributed asynchronous dynamic edge-event-triggered output feedback control protocol. ◆ Proposed control protocol reduces the dimension of information to be transmitted between neighboring agents. ◆ Control protocol can guarantee a larger inter-event time interval and reduce more communication frequency. ◆ Considers time delay for the first time when investigating the distributed collaborative control of redundant manipulators and analyzing their kinematic properties. ◆ Establishes allowable upper bound of time delay based on theoretical analyses and verifies the stability, convergence, and robustness of the designed distributed collaborative controller of redundant manipulators rigorously. ◆ Provides illustrative examples on CoppeliaSim and comparisons to prove the validity of the proposed neural dynamics scheme. ◆ For the robust output containment control problem with the uncertain followers of identical nominal dynamics, based on the internal model principle and the compensator technique, the distributed dynamic state and output feedback control laws were introduced to drive the uncertain followers to enter the convex hull spanned by the leaders under the output regulation framework. Among them, the nonsingular transformation and a Lyapunov inequality method was used to analysis the closed-loop system stabilization. ◆ Instead of handling the entire image in a single network, BaMBNet assigns different regions with different blur amounts into multiple branches with different capacities, which can maintain the information of the clear regions while recovering the missing details of the blurred regions. ◆ Devise an unsupervised learn-based method to estimate the blur amounts of DP image, i.e., COC map. ◆ Use a novel assignment strategy to the estimated COC map to generate the defocus masks, which can effectively and efficiently guide the optimization of multi-branch network. ◆ Expand the application scope of ADRC. This paper is the first attempt of ADRC application in sporadic-in-measurement systems. ◆ New methodology for state estimation with sampled measurements. Different from all existing estimation techniques for systems with sampled measurements, the proposed ESO does not require the knowledge of the nonlinear dynamics and eliminates some restriction on the system nonlinearity. ◆ Rigorous convergence proofs for both the ESO and the closed-loop systems. ◆ Influence of the degradation rate change among different units is explicitly considered. ◆ An age- and state-dependent nonlinear degradation model considering the unit-to-unit variability is proposed. ◆ Uncertainty of the hidden state from the observations is incorporated into the RUL estimation.
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