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Sensing schemes for state estimation in turbulent flows and flexible structures

机译:湍流流动状态估计的传感方案和柔性结构

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We show that with a low number of sensors one can estimate, in real time, the full state of distributed parameter systems like the velocity fields of magnetohydrodynamic flows (liquid metals or plasmas in fusion) or the displacement distribution in cantilever beams (as employed in atomic force microscopy). It suffices to place the sensors at the boundary of the domain of the distributed parameter system-for example, electrodes placed at the wall of an MHD flow, or a laser sensor of the beam tip displacement. The estimation of the infinite-dimensional state of the system is performed using observers designed with the method of "backstepping" which guarantees their exponential convergence to the unsteady motion of the true system. The observer design is performed without any spatial discretization-directly on the non-reduced partial differential equation models. The observer gains do not require the solution of any high-dimensional nonlinear equations, like Riccati equations used in Kalman filters. Instead, the observer gains require the solution of a linear hyperbolic PDE, which is readily solvable both numerically and symbolically. We present simulation results that illustrate the use of our observers.
机译:我们表明,通过较少的传感器可以实时估计,实时估计磁力流体流量的速度场(熔接液体或等离子体的液体金属或等离子体)或悬臂梁中的位移分布(如所用)的分布式参数系统的完整状态(如所用)原子力显微镜)。它足以将传感器放置在分布式参数系统的域的边界处 - 例如,放置在MHD流的壁上的电极,或光束尖端位移的激光传感器。使用具有“BackStepping”方法的观察者来执行系统的无限尺寸状态的估计,其保证其指数趋同对真实系统的不稳定运动。观察者设计在没有任何空间离散化 - 直接在非减少的部分微分方程模型上进行。观察者增益不要求任何高维非线性方程的解决方案,如Kalman滤波器中使用的Riccati方程。相反,观察者收益需要线性双曲线PDE的溶液,其在数值和象征性地易于溶解。我们提出了仿真结果,说明了我们观察者的使用。

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