用ESO和RBFNN进行数据驱动的设定点学习控制,用于不线性批处理的非重复性不确定性
Naseem Ahmad1, Shoulin Hao1, Tao Liu1
1Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China; School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
ISA transactions
|January 10, 2024
概括
本研究介绍了一种数据驱动的控制方法,用于非线性批处理,使用扩展状态观察员 (ESO). 它有效地管理不确定性,并仅使用输入/输出数据优化批量流程.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 控制系统 控制系统
- 过程优化 过程优化
背景情况:
- 非线性批处理过程经常面临非重复性不确定性的挑战.
- 传统的控制方法与未知的动态和未建模的干扰作斗争.
- 数据驱动的方法为复杂的过程控制提供了一个有希望的替代方案.
研究的目的:
- 为非线性批处理过程开发数据驱动的设定点学习控制 (DDSPLC) 方案.
- 仅使用可用的过程输入和输出数据来解决不重复的不确定性.
- 通过自适应设定点调节实现强大的批量优化.
主要方法:
- 使用扩展状态观察器 (ESO) 来估计未知的动态和干扰.
- 采用代动态线性化数据模型 (IDLDM) 来表示过程行为.
- 实现一个辐射基函数神经网络来估计过程信息.
- 设计一个适应性设定点学习控制定律,用于闭环系统优化.
主要成果:
- 拟议的DDSPLC方案有效地处理非线性批处理过程中的非重复性不确定性.
- 在批量方向沿着输出跟踪错误的强大的收被严格证明.
- 该方法通过验证示例展示了对现有方法的有效性和优势.
结论:
- 基于ESO开发的DDSPLC方案为非线性批处理过程控制提供了有效的数据驱动解决方案.
- 这种方法提供了一种实用的方法,可以使用随时可用的数据优化批量流程.
- 该研究验证了拟议的控制策略的稳定性和性能.
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