A multivariable model predictive control (MPC) algorithm is developed for the control and operation of a rapid pressure swing adsorption (RPSA)-based medical oxygen concentrator. The novelty of the approach is the use of all four step durations in the RPSA cycle as independent manipulated variables in a truly multivariable context. The RPSA has a complex, cyclic, nonlinear multivariable operation that requires feedback control, and MPC provides a suitable framework for controlling such a multivariable system. The multivariable MPC presented here uses a quadratic optimization program with integral action and a linear model identified using subspace system identification techniques. The controller was designed and tested in simulation using a complex, highly coupled, nonlinear RPSA process model. The model was developed with the least restrictive assumptions compared to those reported in the literature, thereby providing a more realistic representation of the underlying physical phenomena. The resulting MPC effectively tracks set points, rejects realistic process disturbances and is shown to outperform conventional PID control. © 2017 American Institute of Chemical Engineers AIChE J, 2017

中文翻译：

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新型快速变压吸附系统的多变量模型预测控制

开发了一种多变量模型预测控制（MPC）算法，用于基于快速变压吸附（RPSA）的医用氧气浓缩器的控制和操作。该方法的新颖之处在于，在真正的多变量上下文中，将RPSA循环中的所有四个步骤持续时间用作独立的受控变量。RPSA具有复杂的，循环的，非线性的多变量操作，需要进行反馈控制，而MPC为控制这种多变量系统提供了合适的框架。本文介绍的多变量MPC使用具有积分作用的二次优化程序和使用子空间系统识别技术识别的线性模型。该控制器是使用复杂的，高度耦合的非线性RPSA过程模型进行仿真设计和测试的。与文献报道的模型相比，该模型的开发假设限制最少，因此可以更真实地表示潜在的物理现象。最终的MPC有效地跟踪设定点，排除了实际的过程干扰，并显示出优于常规PID控制的效果。©2017美国化学工程师学会AIChE的Ĵ，2017年