论文标题
非线性网络和系统的关节传感器节点选择和状态估计
Joint Sensor Node Selection and State Estimation for Nonlinear Networks and Systems
论文作者
论文摘要
非线性网络和系统的状态估计和传感器选择问题是无处不在的问题，对于大量工程和物理系统的控制，监视，分析和预测至关重要。对于线性网络，对传感器选择问题进行了广泛的研究。但是，对具有非线性动力学的网络的关注较少。此外，当应用于非线性网络动力学时，依赖于结构（基于图的）可观察性方法的广泛使用的传感器选择方法可能远非最佳结果。此外，状态估计和传感器选择问题通常是单独处理的，这可能会降低总体估计性能。为了应对这些挑战，我们开发了一种新的方法，用于选择具有非线性动力学网络的传感器节点。我们的主要思想是将传感器选择问题纳入初始状态估计问题。所得的混合构成非线性优化问题使用三种方法大致解决。通过测试原型振荡器，联想记忆和化学反应网络的算法，证明了我们方法的良好数值性能。开发的代码可在线提供。
State estimation and sensor selection problems for nonlinear networks and systems are ubiquitous problems that are important for the control, monitoring, analysis, and prediction of a large number of engineered and physical systems. Sensor selection problems are extensively studied for linear networks. However, less attention has been dedicated to networks with nonlinear dynamics. Furthermore, widely used sensor selection methods relying on structural (graph-based) observability approaches might produce far from optimal results when applied to nonlinear network dynamics. In addition, state estimation and sensor selection problems are often treated separately, and this might decrease the overall estimation performance. To address these challenges, we develop a novel methodology for selecting sensor nodes for networks with nonlinear dynamics. Our main idea is to incorporate the sensor selection problem into an initial state estimation problem. The resulting mixed-integer nonlinear optimization problem is approximately solved using three methods. The good numerical performance of our approach is demonstrated by testing the algorithms on prototypical Duffing oscillator, associative memory, and chemical reaction networks. The developed codes are available online.
