基于简化整定机构的复杂动态过程预测函数控制

Muhammad Saleheen Aftab, John Anthony Rossiter, G. Panoutsos
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摘要

预测函数控制(PFC)是一种廉价的简化模型预测控制器,在价格和性能上与PID相竞争。虽然PFC中针对简单动力学的调谐过程已经很好地建立和直接,但对于表现出具有挑战性行为的过程,例如差阻尼,不稳定性和/或非最小相位特性,它变得更加模糊,并且通常不太有效。在本文中,我们提出了一种相对的PFC算法,当需要时使用预稳定的预测动态实现时,将性能调优简化为仅调整一个参数。此外,它在实际场景中提供了卓越的闭环控制,传统的PFC和PID无法执行,如三个仿真案例研究所示。
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Predictive Functional Control for Difficult Dynamic Processes with a Simplified Tuning Mechanism
Predictive functional control (PFC) is a cheap and simplified model predictive controller, which competes with PID in price and performance. While the tuning process in PFC for simple dynamics is well established and straightforward, it becomes far more ambiguous and often less effective for processes exhibiting challenging behaviour, such as poor damping, instability and/or non-minimum phase characteristics. In this paper, we present a relative PFC algorithm that, when implemented with pre-stabilised prediction dynamics if needed, simplifies performance tuning to merely adjusting one parameter. Furthermore, it provides far superior closed-loop control in practical scenarios, where the conventional PFC and PID fail to perform, as demonstrated with three simulation case studies.
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