Self-Tuning Network Control Architectures
Self-Tuning Network Control Architectures
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DOI：
10.1109/cdc51059.2022.9992780
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
T. Summers;Karthik Ganapathy;I. Shames;Mathias Hudoba de Badyn
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文献类型：
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作者：
T. Summers;Karthik Ganapathy;I. Shames;Mathias Hudoba de Badyn
给出了自校正网络控制体系结构设计的一般数学框架。该问题涉及联合调整网络中有源传感器和执行器的位置以及反馈控制策略，以适应关于时变网络状态和动态的所有可用信息，以优化性能标准。从自适应控制出发，提出了一种类似于经典自校正调节器的通解结构。我们证明了具有全状态反馈的特殊情况原则上可以用动态规划来解决，并且在线性二次设置下，最优代价函数和策略分别是分段二次和分段线性的。对于穷举体系结构搜索被禁止的大型网络，我们描述了用于联合体系结构策略设计的贪婪启发式方法。我们在数值实验中证明了自调优体系结构可以提供比固定体系结构显著提高的性能。我们的一般公式提供了一个极其丰富和具有挑战性的问题空间，有机会应用从随机控制、系统辨识、强化学习和静态体系结构设计的各种近似方法。
We formulate a general mathematical framework for self-tuning network control architecture design. This problem involves jointly adapting the locations of active sensors and actuators in the network and the feedback control policy to all available information about the time-varying network state and dynamics to optimize a performance criterion. We propose a general solution structure analogous to the classical self-tuning regulator from adaptive control. We show that a special case with full-state feedback can be solved in principle with dynamic programming, and in the linear quadratic setting the optimal cost functions and policies are piecewise quadratic and piecewise linear, respectively. For large networks where exhaustive architecture search is prohibitive, we describe a greedy heuristic for joint architecture-policy design. We demonstrate in numerical experiments that self-tuning architectures can provide dramatically improved performance over fixed architectures. Our general formulation provides an extremely rich and challenging problem space with opportunities to apply a wide variety of approximation methods from stochastic control, system identification, reinforcement learning, and static architecture design.