• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IFAC PapersOnLine >An Adaptive Correction Scheme for Offset-Free Asymptotic Performance in Deep Learning-based Economic MPC ?
【24h】

An Adaptive Correction Scheme for Offset-Free Asymptotic Performance in Deep Learning-based Economic MPC ?

机译：基于深度学习的经济MPC中无偏移渐近性能的自适应校正方案 ？

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

There has been an increasing interest in explicit and cheap-to-evaluate control policies that approximate (computationally expensive) control laws such as model predictive control (MPC). However, approximate control policies are subject to approximation errors, leading to asymptotic performance losses. The contribution of this paper is three-fold: (i) a closed-loop training scheme is presented for deep neural network approximation of economic MPC; (ii) an online adaptive correction scheme is presented to account for the performance losses induced by approximation errors; and (iii) an offline performance verification scheme is presented to ensure that the approximate control policy converges to an equilibrium point of the system. The proposed approach is illustrated using a Williams-Otto reactor problem.
机译：对近似（计算昂贵的）控制法律（如模型预测控制（MPC））的控制策略越来越兴趣。 然而，近似控制策略受到近似误差的影响，导致渐近性能损失。 本文的贡献是三倍：（i）提出了闭环训练方案，用于经济MPC的深度神经网络近似; （ii）提出了一种在线自适应校正方案，以解释近似误差引起的性能损失; （iii）提出了离线性能验证方案，以确保近似控制策略会聚到系统的均衡点。 使用WILLIAMS-OTTO反应堆问题说明所提出的方法。

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