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基于遗传算法优化的注射成型批量过程部分执行器故障的改进状态空间模型预测容错控制。

Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization.

机构信息

The Belt and Road Information Research Institute, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China.

The Belt and Road Information Research Institute, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Key Laboratory of Advanced Control and Optimization for Chemical Processes, Shanghai 200237, China.

出版信息

ISA Trans. 2018 Feb;73:147-153. doi: 10.1016/j.isatra.2017.12.022. Epub 2018 Jan 3.

Abstract

A novel model predictive fault-tolerant control (MPFTC) strategy adopting genetic algorithm (GA) is proposed for batch processes under the case of disturbances and partial actuator faults. Based on the extended state space model in which the tracking error is contained, there are more degrees of freedom provided for the controller design and better control performance is obtained. In order to enhance the control performance further, the GA is introduced to optimize the relevant weighting matrices in the cost function. The effectiveness of the proposed MPFTC approach is tested on the injection velocity regulation of the injection molding process.

摘要

针对存在干扰和部分执行器故障的间歇过程,提出了一种基于遗传算法(GA)的新型模型预测容错控制(MPFTC)策略。基于包含跟踪误差的扩展状态空间模型,为控制器设计提供了更多的自由度,从而获得了更好的控制性能。为了进一步提高控制性能,引入了 GA 来优化代价函数中的相关加权矩阵。所提出的 MPFTC 方法在注塑过程的注射速度调节中进行了有效性测试。

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