NONLINEAR MODEL PREDICTIVE CONTROL FOR THE ALSTOM GASIFIER BENCHMARK PROBLEM
NONLINEAR MODEL PREDICTIVE CONTROL FOR THE ALSTOM GASIFIER BENCHMARK PROBLEM
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DOI：
10.3182/20050703-6-cz-1902.01587
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发表时间：
2006-09
期刊：
影响因子：
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通讯作者：
R. A. Seyab;Yi Cao
中科院分区：
文献类型：
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作者：
R. A. Seyab;Yi Cao
模型预测控制因其直观且能够明确地处理具有变量约束和相互作用的MIMO线性和非线性系统而成为工业控制策略的首选。本文建立了一个非线性状态空间模型，并将其作为预测控制的内部模型应用于阿尔斯通气化炉。采用电厂0%负荷时的线性模型作为预测的基本模型。其次，针对燃气压力这一特定输出通道建立了静态非线性神经网络模型，以补偿其在开环仿真中观察到的强非线性行为。通过在每个采样时间对神经网络模型进行线性化，静态非线性模型对线性基模型具有一定的适应性。与基于纯线性模型的预测控制相比，性能有明显提高。
Model predictive control has become a first choice control strategy in industry because it is intuitive and can explicitly handle MIMO linear and nonlinear systems with the presence of variable constraints and interactions. In this work a nonlinear state-space model has been developed and used as the internal model in predictive control for the ALSTOM gasifier. A linear model of the plant at 0% load is adopted as a base model for prediction. Secondly, a static nonlinear neural network model has been created for a particular output channel, fuel gas pressure, to compensate its strong nonlinear behaviour observed in open-loop simulation. By linearizing the neural network model at each sampling time, the static nonlinear model provides certain adaptation to the linear base model. Noticeable performance improvement is observed when compared with pure linear model based predictive control.