Strategies for Computing Switching Feedback Controllers

Wernrud, Andreas

doi:10.1109/acc.2007.4283079

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…However, for a nonlinear system, the Hamilton-Jacobi-Bellman (HJB) equation has to be solved to obtain the estimated optimal solution, instead of the ARE, which serves as a foundation of this technique in synthesizing a dynamic controller. The PIA controller has been applied in some practical examples: a DC-DC converter (Wernrud (2007)), a power plant for the optimal-load-frequency controller (Wang et al (1993)), a hybrid system of a jumping robot (Suda and Yamakita (2013)), a double-link pendulum for swing-up control and an adaptive steering control of a tanker ship (Xu et al (2007)). Therefore, the PIA should work out in a closed-loop control of oxygenation using mechanical ventilation therapy.…”

Section: Introductionmentioning

confidence: 99%

Policy Iteration Algorithm for the Control of Oxygenation

et al. 2015

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The policy iteration algorithm (PIA) is a quasi non-identifier approach of nonlinear optimal control based on a reinforcement learning and iterative algorithm in order to solve the Hamilton-Jacobi-Bellman (HJB) equation. The synthesized state-feedback controller corresponding to the converged solution should be applicable for the control of cardiopulmonary system. In this article, the simulation results for the control of oxygenation were carried out using a simplified first-order model with time delay based on porcine dynamics. The distinctive results of oxygenation control can then be achieved based on the proposed control strategy. In addition, the practical example of water level for interacting three-tank system, which has the nonlinear dynamics similar to that of the oxygenation, was implemented in order to prove the concept of this control scheme.

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