Performance - Based Switching Control Systems

ECS-9812213 Krogh 在本研究计画中，将探讨一种新的切换控制器。 给定用于工厂的多个控制器，每个控制器被设计用于具有不同的稳定性和性能特性区域的可能不同的操作区域，将根据对每个控制器的未来潜在闭环系统性能的在线评估来选择要在每个采样时刻应用的控制器。 开关控制器将实现比单独使用任何一个控制器所能实现的更大的操作区域和更好的性能。 将训练神经网络，以使用神经动态编程技术根据实验数据估计成本函数和稳定区域。 将研究状态反馈和输出反馈控制器，以及在线学习性能指标的算法。 策略迭代技术将被研究用于优化切换策略。 闭环稳定性和性能将研究在混合动力系统的稳定性和不变集的理论。 开关控制策略将使用仿真来证明。

ECS-9812213 Krogh In this research project a new class of switching controllers will be investigated. Given multiple controllers for a plant, each designed for possibly different operating regions with distinct regions of stability and performance characteristics, the controller to be applied at each sampling instant will be selected according to on-line evaluations of the future potential closed-loop system performance for each of the controllers. The switching controller will achieve a larger operating region and better performance than could be realized using any one of the controllers alone. Neural networks will be trained to estimate cost-to-go functions and stability regions based on experimental data using techniques from neural dynamic programming. State feedback and output feedback controllers will be studied as will as algorithms for on-line learning of performance measures. Policy iteration techniques will be investigated for optimizing the switching strategies. Closed-loop stability and performance will be studied drawing on the theory of stability and invariant sets in hybrid dynamic systems. The switching control strategies will be demonstrated using simulation.