...
首页> 外文期刊>Journal of Dynamic Systems, Measurement, and Control >Adaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header
【24h】

Adaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header

机译:带有标题的并行采购公用事业单位的最优预定多个模型的自适应通用预测控制

获取原文
获取原文并翻译 | 示例
           

摘要

An adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the Takagi–Sugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications.
机译:提出了一种使用最优调度多模型(OSMM-GPC)的自适应通用预测控制,以提高带有标题的并行咨询公用事业单位系统(PUUH)的负荷跟踪能力和经济效益。 OSMM-GPC是一种基于分布式多模型控制体系结构的综合控制算法。它通过两种新颖的算法从一般预测控制中得到了改进。一种是混合模糊递归最小二乘(MFRLS)估计,另一种是模型最优调度算法。 MFRLS通过在每个采​​样点的隶属度特征上对动态多目标成本函数加权来混合本地和全局在线估计。它在时变系统的Takagi-Sugeno(TS)模糊模型上提供了比局部和全局递归最小二乘更好的参数估计,因此,它适合为OSMM-GPC建立自适应模型。基于MFRLS估计的高精度自适应模型,模型最优调度算法计算所有控制组的调节效率,然后选择负责多变量一般预测控制的最优模型。通过在每个操作点进行模型调度,可以节省大量燃油。同时,获得了更好的控制性能。除了PUUH,OSMM-GPC还可以用于其他分布式多模型控制应用程序。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号