同时的模型预测和数据驱动的控制,在模型上放松假设
Roozbeh Abolpour1, Alireza Khayatian1, Maryam Dehghani1
1School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
ISA transactions
|January 20, 2024
概括
本研究引入了使用时间序列数据进行模型预测控制 (MPC) 的新方法. 它简化了复杂的优化问题,以更好地控制系统和参数估计.
科学领域:
- 控制系统工程 控制系统工程
- 时间序列分析时间序列分析
- 优化理论 优化理论
背景情况:
- 模型预测控制 (MPC) 对于先进的系统调节至关重要.
- 准确的系统识别和控制信号设计往往是具有挑战性的,因为未知的参数和动态.
- 现有的方法可能会与复杂的,非凸的优化问题作斗争.
研究的目的:
- 使用系统输出-输出时间序列数据开发一种新的模型预测控制 (MPC) 规律.
- 同时识别未知的自动回归集成移动平均 (ARIMA) 模型参数和控制器信号序列.
- 将一个非凸的优化问题重新构成一个更易于处理的形式,以便有效地解决.
主要方法:
- 利用来自系统输入和输出的时间序列数据.
- 在数据窗口中制定一个优化问题,以确定未知的模型参数和控制器信号.
- 将一个具有非凸约束的非凸优化问题转换为具有凸约束和非凸目标函数的等效问题.
主要成果:
- 拟议的转换简化了优化问题,使其可以用当前的解决方案来解决.
- 通过各种例子证明了开发的MPC方法的有效性.
- 该方法在同时估计模型参数和设计控制信号方面取得了令人满意的结果.
结论:
- 提出的方法提供了一种有效的方法,可以从时间序列数据中设计MPC规律.
- 转换技术显著缓解了系统识别和控制中的复杂优化问题的解决方案.
- 该研究验证了拟议方法在现实场景中的实际适用性和说服力.
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