Towards Understanding Sensor and Control Nodes Selection in Nonlinear Dynamic Systems: Lyapunov Theory Meets Branch-and-Bound
Towards Understanding Sensor and Control Nodes Selection in Nonlinear Dynamic Systems: Lyapunov Theory Meets Branch-and-Bound
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DOI：
10.1016/j.automatica.2021.109904
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Sebastian A. Nugroho;A. Taha
中科院分区：
文献类型：
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作者：
Sebastian A. Nugroho;A. Taha
传感器与执行器的选择问题是动态系统设计与控制中的核心问题之一。这些问题对应于确定传感器（测量）或执行器（控制节点）的最佳选择，以便实现某些估计/控制目标。虽然关于sasp的文献确实是根深蒂固的，但绝大多数工作都集中在网络动力学的线性（化）表示上，导致传感器或执行器（SAs）的放置在有限的操作区域有效。作为替代方案，本文提出了一个新的通用框架来解决非线性动态系统（nds）中的sasp，假设输入和输出与非线性动力学线性耦合。通过(i)将nss分类并参数化为各种非线性函数集，（ii）利用丰富的Lyapunov理论公式，以及（iii）设计一种新的定制分支定界（BnB）算法来研究这一点，该算法利用sasp的问题结构。新设计的BnB例程在计算上比标准例程更具吸引力，并且可以直接适用于求解线性系统的sasp。与文献中的当代方法相比，我们的方法适用于寻找稳定/不稳定nds的最优sa组合，通过简单的线性反馈控制策略确保估计误差和闭环动力学的稳定。
Sensor and actuator selection problems (SASPs) are some of the core problems in dynamic systems design and control. These problems correspond to determining the optimal selection of sensors (measurements) or actuators (control nodes) such that certain estimation/control objectives can be achieved. While the literature on SASPs are indeed inveterate, the vast majority of the work focuses on linear(ized) representation of the network dynamics, resulting in the placements of sensors or actuators (SAs) that are valid for confined operating regions. As an alternative, herein we propose a new general framework for addressing SASPs in nonlinear dynamic systems (NDSs), assuming that the inputs and outputs are linearly coupled with the nonlinear dynamics. This is investigated through(i)classifying and parameterizing the NDSs into various nonlinear function sets,(ii)utilizing rich Lyapunov theoretic formulations, and(iii)designing a new customized branch-and-bound (BnB) algorithm that exploits problem structure of the SASPs. The newly designed BnB routines are computationally more attractive than the standard one and also directly applicable to solve SASPs for linear systems. In contrast with contemporary approaches from the literature, our approach is suitable for finding the optimal SAs combination for stable/unstable NDSs that ensures stabilization of estimation error and closed-loop dynamics through a simple linear feedback control policy.