适应模型预测控制方案的实验和模拟调查:模型通过正则基函数参数化
1Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
本研究引入了一个自适应模型预测控制 (AMPC) 方案,该方案实时更新其内部模型. 这种自适应模型预测控制方法确保在工业过程中在不断变化的操作条件下保持一致的性能.
科学领域:
- 过程控制 过程控制
- 适应性系统 适应性系统
- 化学工程是化学工程的重要组成部分.
背景情况:
- 模型预测控制 (MPC) 的性能取决于准确的预测模型.
- 在连续工厂中常见的线性MPC模型,在显著的操作点转移时会出现动摇.
- 为了保持MPC的有效性,需要更新预测模型以适应变化的名义条件.
研究的目的:
- 设计一个自适应模型预测控制 (AMPC) 方案.
- 使用从输入-输出扰动数据估计的线性模型.
- 在动态工业环境中解决固定模型MPC的局限性.
主要方法:
- 开发了一个适应性MPC (AMPC) 方案,使用OBF-ARX (具有ARX结构的一般化或正常基础过器) 进行观察者动态.
- 在名义运行条件下的扰动数据估计的线性模型.
- 提出了固定杆和可变杆AMPC方案.
主要成果:
- 通过对二元蒸柱的模拟来证明AMPC的有效性.
- 通过对两加热器设置的实验研究验证了AMPC性能.
- 在伺服器 (设置点跟踪) 和调节器 (干扰排斥) 问题上都得到了证实.
结论:
- 拟议的AMPC方案在不断变化的操作条件下有效地保持控制性能.
- AMPC成功地跟踪设定点并拒绝动态系统中的干扰.
- 这些自适应模型预测控制策略显示出精确工业过程控制的巨大潜力.
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