基于多优先级稳态优化的双层结构预测控制算法及软件实现

潘红光; 高海南; 孙耀; 张英; 丁宝苍

doi:10.3724/SP.J.1004.2014.00405

基于多优先级稳态优化的双层结构预测控制算法及软件实现

doi: 10.3724/SP.J.1004.2014.00405

1.
西安交通大学智能网络与网络安全教育部重点实验室电子与信息工程学院自动化系西安 710049

基金项目:

国家自然科学基金（61174095），工业控制技术国家重点实验室（ICT1213）资助

详细信息

作者简介:
潘红光西安交通大学博士研究生. 主要研究方向为预测控制.E-mail：hongguangpan@163.com

通讯作者:
丁宝苍

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出版历程
- 收稿日期: 2012-12-24
- 修回日期: 2013-05-23
- 刊出日期: 2014-03-20

The Algorithm and Software Implementation for Double-layered Model Predictive Control Based on Multi-priority Rank Steady-state Optimization

1.
Key Laboratory for Intelligent Networks and Network Security, Ministry of Education (MOE KLINNS), Department of Automation, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049

Funds:

Supported by National Natural Science Foundation of China (61174095) and the State Key Laboratory of Industrial Control Technology (ICT1213)

摘要

摘要: 研究包含稳态目标计算（Steady-state target calculation，SSTC）层和动态控制层的双层结构预测控制（Model predictive control，MPC）及其实现方法. 我们将已有的辨识、优化和控制方案适当地组合并软件化. 通过在多优先级稳态目标计算中引入新的变量，给出了稳态目标计算的统一表达方法，每个优先级的优化问题或是跟踪外部目标，或是放松软约束. 通过仿真算例和应用实例相结合的方式验证了软件功能.
- 预测控制 /
- 多优先级策略 /
- 稳态目标计算 /
- 动态控制
Abstract: This paper studies the model predictive control (MPC) with multi-priority rank steady-state target calculation (SSTC) and dynamic control, and its implementation method. We properly combine the methods of identification, optimization and control in the existing literature, and implement these methods by software. By introducing new variables, the SSTC can be represented in a unified method, and the optimization problem in each priority rank is either the external target tracking or the soft constraints softening. We verify the software functions of the multi-priority rank SSTC via simulation and application examples.
- Model predictive control (MPC) /
- multi-priority rank strategy /
- steady-state target calculation (SSTC) /
- dynamic control

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