Observer-Based Control of LPV Systems with Input Delay and Saturation and Matched Disturbances via a Generalized Sector Condition

Saeed Salavati, Karolos Grigoriadis , Matthew Franchek 
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Abstract

This paper examines the control design for parameter-dependent input-delay linear parameter-varying (LPV) systems with saturation constraints and matched input disturbances. A gain-scheduled dynamic output feedback controller, coupled with a disturbance observer to cancel out input disturbance effects, was augmented with an anti-windup compensator to locally stabilize the input-delay LPV system under saturation, model uncertainty, and exogenous disturbances. Sufficient delay-dependent conditions to asymptotically stabilize the closed-loop system were derived using Lyapunov-Krasovskii functionals and a modified generalized sector condition to address the input saturation nonlinearity. The level of disturbance rejection was characterized via the closed-loop induced L 2 -norm of the closed-loop system in the form of linear matrix inequality (LMI) constraints. The results are examined in the context of the mean arterial pressure (MAP) control in the clinical resuscitation of critical hypotensive patients. The MAP variation response to the injection of vasopressor drugs was modeled as an LPV system with a varying input delay and was susceptible to model uncertainty and input/output disturbances. A Bayesian filtering method known as the cubature Kalman filter (CKF) was used to estimate the instantaneous values of the parameters. The varying delay was estimated via a multiple-model approach. The proposed input-delay LPV control was validated in closed-loop simulations to demonstrate its merits and capabilities in the presence of drug administration constraints.
广义扇区条件下具有输入延迟、饱和和匹配扰动的LPV系统观测器控制
研究了具有饱和约束和匹配输入扰动的参数相关输入延迟线性参数变化(LPV)系统的控制设计。增益调度动态输出反馈控制器与扰动观测器耦合以抵消输入扰动效应,并增加了抗饱和补偿器,以在饱和、模型不确定性和外部扰动下局部稳定输入延迟LPV系统。利用李亚普诺夫-克拉索夫斯基泛函和一个修正的广义扇形条件来解决输入饱和非线性问题,得到了闭环系统渐近稳定的充分的时滞相关条件。通过线性矩阵不等式(LMI)约束形式的闭环系统的闭环诱导L2-范数来表征扰动抑制水平。这些结果是在危重低血压患者临床复苏中的平均动脉压(MAP)控制的背景下进行检查的。对血管升压药物注射的MAP变化响应被建模为具有变化输入延迟的LPV系统,并且容易受到模型不确定性和输入/输出扰动的影响。使用称为容积卡尔曼滤波器(CKF)的贝叶斯滤波方法来估计参数的瞬时值。通过多模型方法估计了变化的延迟。在闭环仿真中验证了所提出的输入延迟LPV控制,以证明其在存在给药约束的情况下的优点和能力。
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