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Optimal Sensor and Actuator Scheduling in Sampled-Data Control of Spatially Distributed Processes ?

机译：空间分布过程的采样数据控制中的最佳传感器和执行器调度 ？

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摘要

This work presents an optimization-based methodology for the placement and scheduling of measurement sensors and control actuators in spatially-distributed processes with low-order dynamics and discretely-sampled output measurements. Initially, a sampled-data observer-based controller, with an inter-sample model predictor, is designed based on an approximate finite-dimensional system that captures the infinite-dimensional system’s dominant dynamics. An explicit characterization of the interdependence between the stabilizing locations of the sensors and actuators and the maximum allowable sampling period is obtained. Based on this characterization, a constrained finite-horizon optimization problem is formulated to obtain the sensor and actuator locations, together the corresponding sampling period, that optimally balance the tradeoff between the control performance requirements on the one hand, and the demand for reduced sampling, on the other. The objective function penalizes both the control performance cost, expressed in terms of the response speed and the control effort, and the sampling cost, expressed in terms of the sampling frequency. The optimization problem is solved in a receding horizon fashion, leading to a dynamic policy that varies the sensor and actuator spatial placement, together with the sampling period, over time. The developed methodology is illustrated through an application to a simulated diffusion-reaction process example.
机译：这项工作提出了一种基于优化的方法，用于在具有低阶动力学和离散采样输出测量的空间分布过程中测量传感器和控制执行器的放置和调度。最初，基于采样数据观测器的控制器（带有样本间模型预测器）是基于近似有限维系统设计的，该系统捕获了无限维系统的主导动力学。获得传感器和执行器的稳定位置与最大允许采样周期之间的相互依赖性的明确特征。基于此特征，提出了一个受约束的有限水平优化问题，以获取传感器和执行器的位置，以及相应的采样周期，从而可以在一方面控制性能要求与减少采样需求之间取得最佳平衡，在另一。目标函数对以响应速度和控制工作量表示的控制性能成本以及以采样频率表示的采样成本进行惩罚。优化问题以后退的方式解决，导致了一种动态策略，该策略会随着时间的推移改变传感器和执行器的空间位置以及采样周期。通过在模拟扩散反应过程示例中的应用说明了所开发的方法。

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