使用经典和并行维纳模型进行快速非线性预测控制:对中和反应堆过程进行比较
Robert Nebeluk1, Maciej Ławryńczuk1
1Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
平行维纳模型提高模型预测控制 (MPC) 预测准确性和控制质量. 本研究引入了使用并行维纳模型的快速MPC算法,在中和基准过程中显示了更好的性能.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 控制系统工程 控制系统工程
- 计算建模 计算建模
背景情况:
- 维纳模型是线性和非线性块的连续组合,对于模型预测控制 (MPC) 中的预测至关重要.
- 与传统的系列配置相比,平行维纳模型结构在建模准确性和MPC控制质量方面提供了潜在的改进.
研究的目的:
- 提出一个快速的模型预测控制 (MPC) 算法,利用并行维纳模型进行在线预测.
- 通过中和基准过程,研究模型结构对建模准确性的影响.
- 在MPC框架内评估并行维纳模型的闭环控制效率.
主要方法:
- 开发了一种快速的MPC算法,具有在线轨迹线性化,用于计算高效的二次优化.
- 在开放循环模式下对经典和并行维纳模型结构进行比较分析,用于中和基准.
- 使用平行Wiener模型对中和过程的闭环MPC性能的验证.
主要成果:
- 与经典维纳模型相比,平行维纳模型结构在开放循环模式下显示出明显较低的预测错误.
- 在闭环MPC中,并行维纳模型显示了中和过程的控制质量指标的改善.
- 虽然观察到改善,但经典和并行维纳模型之间的控制质量差异在统计学上并不显著.
结论:
- 并行维纳模型提供了更高的预测准确性和在MPC应用中改善控制的潜力.
- 开发的快速MPC算法有效地利用并行维纳模型进行在线预测和优化.
- 可能需要进一步的研究,以充分阐明闭环系统中控制质量差异的重要性.
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