基于极值寻优控制与自优化控制的两阶段厌氧消化过程最优控制研究

Hongxuan Li, Yang Tian, Haoping Wang
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引用次数: 1

摘要

本文设计了一种新的基于动态非线性梯度观测器的极值寻求控制算法(DNGO-based ESC)和基于动态雅比矩阵估计器的自优化控制算法(dji -based SOC),用于两级厌氧消化(TSAD)的控制。这两种算法都不需要关于系统模型的先验知识。将所提算法与经典的极值搜索控制算法和基于卡尔曼滤波的牛顿极值搜索控制算法进行了比较。仿真结果表明,在存在干扰的情况下,两种控制算法均能使系统保持在最优工作点,并将氢气和甲烷产量驱动到极值点。未来的工作是在实际的两阶段厌氧消化过程中验证所设计的控制算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-stage Anaerobic Digestion Process Optimal Control Study based on Extremum-seeking Control and Self-optimizing Control
In this paper, a new dynamic nonlinear gradient observer-based extremum-seeking control algorithm (DNGO-based ESC) and a dynamic Jacobian matrices estimator-based self-optimizing control algorithm (DJE-based SOC) are designed for the control of two-stage anaerobic digestion (TSAD). None of two algorithms requires priori knowledge about the system model. The proposed algorithms are compared with the classical extremum-seeking control algorithm and the Kalman Filter based Newton extremum-seeking control algorithm. The simulation results show that in the presence of disturbance both of proposed control algorithms can maintain the system at the optimal operating point and drive the hydrogen and methane yields to the extreme point. Future work is to validate the designed control algorithm in an actual two-stage anaerobic digestion process.
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