Optimal learning control of oxygen saturation using a policy iteration algorithm and a proof-of-concept in an interconnecting three-tank system

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• 作者：Anake Pomprapa ; ^{pomprapa@hia.rwth-aachen.de} ; Steffen Leonhardt ; Berno J.E. Misgeld
• 关键词：Policy iteration algorithm ; Optimal control ; Reinforcement learning ; Control of oxygen saturation ; Biomedical control system ; Closed-loop ventilation
• 刊名：Control Engineering Practice
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：59
• 期：Complete
• 页码：194-203
• 全文大小：2966 K
• 卷排序：59

文摘

In this work, “policy iteration algorithm” (PIA) is applied for controlling arterial oxygen saturation that does not require mathematical models of the plant. This technique is based on nonlinear optimal control to solve the Hamilton–Jacobi–Bellman equation. The controller is synthesized using a state feedback configuration based on an unidentified model of complex pathophysiology of pulmonary system in order to control gas exchange in ventilated patients, as under some circumstances (like emergency situations), there may not be a proper and individualized model for designing and tuning controllers available in time. The simulation results demonstrate the optimal control of oxygenation based on the proposed PIA by iteratively evaluating the Hamiltonian cost functions and synthesizing the control actions until achieving the converged optimal criteria. Furthermore, as a practical example, we examined the performance of this control strategy using an interconnecting three-tank system as a real nonlinear system.