Abstract:

In practice, model-plant mismatch(MPM) is a key factor that results in performance deterioration in model predictive control(MPC). Traditional correlation analysis between the prediction residual and the manipulated variable of a channel is usually affected by other manipulated variables and disturbance. The result of this process is unreliable, thus unable to locate the MPM accurately. Based on the above problems, partial correlation analysis is used to calculate the correlation between the prediction residual and the manipulated variable of each channel, under the premise of removing the effect of other manipulated variables and disturbance. The MPM problem is converted to a distribution problem of partial correlation coefficients in a certain interval. Whether a channel is mismatched is judged by observing the distribution graph of partial correlation coefficients. The experimental validation on the Shell tower demonstrates the effectiveness of this method.

Key words: model-plant mismatch, model predictive control, correlation analysis, partial correlation analysis, experimental validation

摘要：

在实际中,模型失配是导致模型预测控制性能下降的关键因素。传统的相关性分析方法在分析某一通道的预测残差和操作变量之间的相关性时,会受到其他操作变量及干扰的影响,导致结果不可靠,无法准确定位失配。针对上述问题,现采用偏相关性分析方法。在滤除其他操作变量和干扰影响的前提下,分析各通道预测残差和操作变量之间的相关性,将模型失配问题转化为一定区间上偏相关系数的分布问题。通过观察各通道偏相关系数的分布图判断是否发生失配,并通过Shell塔实验验证该方法的有效性。

关键词: 模型失配, 模型预测控制, 相关性分析, 偏相关性分析, 实验验证