论文标题
实时投影基于梯度的非线性模型预测控制，并应用于麻醉控制
Real-time Projected Gradient-based Nonlinear Model Predictive Control with an Application to Anesthesia Control
论文作者
论文摘要
医学药物输液问题构成了诸如生理模型的非线性，由于患者间和患者内变异性引起的不确定性以及严格的安全规范的挑战。考虑到这些挑战，我们提出了一种基于预计梯度下降迭代的新型实时非线性模型预测控制（NMPC）方案。在每次迭代中，都采取了沿NMPC成本梯度的少量步骤，产生了次优输入，该输入渐近地收敛到最佳输入。我们通过执行足够数量的梯度迭代来检索经典的Lyapunov稳定性，直到达到停止标准为止。这种实时控制方法允许从系统中提高采样率和更快的反馈，这对于高度可变和不确定的药物输注问题是有利的。为了证明控制器的潜力，我们将其应用于两种相互作用药物的麻醉中的催眠控制。控制器即使在干扰和不确定性下也成功地调节了催眠，并符合基准性能标准。
Medical drug infusion problems pose a combination of challenges such as nonlinearities from physiological models, model uncertainty due to inter- and intra-patient variability, as well as strict safety specifications. With these challenges in mind, we propose a novel real-time Nonlinear Model Predictive Control (NMPC) scheme based on projected gradient descent iterations. At each iteration, a small number of steps along the gradient of the NMPC cost is taken, generating a suboptimal input which asymptotically converges to the optimal input. We retrieve classical Lyapunov stability guarantees by performing a sufficient number of gradient iterations until fulfilling a stopping criteria. Such a real-time control approach allows for higher sampling rates and faster feedback from the system which is advantageous for the class of highly variable and uncertain drug infusion problems. To demonstrate the controller's potential, we apply it to hypnosis control in anesthesia of two interacting drugs. The controller successfully regulates hypnosis even under disturbances and uncertainty and fulfils benchmark performance criteria.
