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Summary In this paper, the problem of formation control is considered for a class of unknown nonaffine nonlinear multiagent systems under a repeatable operation environment. To achieve the formation objective, the unknown nonlinear agent's dynamic is first transformed into a compact form dynamic linearization model along the iteration axis. Then, a distributed model‐free adaptive iterative learning control scheme is designed to ensure that all agents can keep their desired deviations from the reference trajectory over the whole time interval. The main results are given for the multiagent systems with fixed communication topologies and the extension to the switching topologies case is also discussed. The feature of this design is that formation control can be solved only depending on the input/output data of each agent. An example is given to demonstrate the effectiveness of the proposed method.

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Abstract-In modern Cloud Data Centers (DC)s, correct implementation of network policies is crucial to provide secure, efficient and high performance services for tenants. It is reported that the inefficient management of network policies accounts for 78% of DC downtime, challenged by the dynamically changing network characteristics and by the effects of dynamic Virtual Machine (VM) consolidation. While there has been significant research in policy and VM management, they have so far been treated as disjoint research problems.In this paper, we explore the simultaneous, dynamic VM and policy consolidation, and formulate the Policy-VM Consolidation (PVC) problem, which is shown to be NP-Hard. We then propose Sync, an efficient and synergistic scheme to jointly consolidate network policies and virtual machines. Extensive evaluation results and a testbed implementation of our controller show that policy and VM migration under Sync significantly reduces flow endto-end delay by nearly 40%, and network-wide communication cost by 50% within few seconds, while adhering strictly to the requirements of network policies.

This paper presents a stability analysis of the iterative learning control for discretetime systems with data quantization. Three quantized iterative learning control schemes are considered by using different quantized signals, including system output quantized signal, tracking error quantized signal, and control input quantized signal. The logarithmic quantizer is introduced to decode these signals with a number of quantization levels, and the sector bound method is used to deal with the quantization error. Based on the supervector formulation for iterative learning control systems, some convergence conditions for these iterative learning control laws are given, respectively. It is shown that iterative learning control laws with system output quantized signal and control input quantized signal only guarantee that the tracking error converges to a bound and the bound depending on quantization density and desired trajectory. Thus, the iterative learning control law with tracking error quantized signal can obtain zero tracking error. These results are illustrated by 2 examples.

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This paper is devoted to the study of the exact controllability for a one-dimensional wave equation in domains with moving boundary. This equation characterizes the motion of a string with a fixed endpoint and the other a moving one. The control is put on the fixed endpoint. When the speed of the moving endpoint is less than the characteristic speed, by the Hilbert uniqueness method (HUM), exact controllability of this equation is established.