Abstract:

The performance of model-predictive control (MPC) is affected by many factors. Considering that current model quality index does not take the influence of disturbance into consideration, two indices are applied to realize real-time monitoring of system performance: historical performance index based on MPC objective function and covariance index based on model predictive residuals. The former monitors the whole performance and the latter reflects the influence of the model mismatch and the disturbance. They respond differently to different factors. Combining the re-identified result of the disturbance innovations, we can get preliminary diagnosis why the system performance decreases and narrow the scope of the source of performance degradation. Finally, the experimental validation on Wood-berry column demonstrates the effectiveness of this method.

Key words: model-predictive control, historical performance index, model predictive residuals, covariance index, real-time monitoring, experimental validation

摘要：

模型预测控制的性能受多种因素的影响,现有的模型质量评价指标没有考虑外界干扰的变化,反映系统整体性能时不够全面。针对上述问题,现结合两个指标:基于模型预测控制目标函数的历史性能指标和基于模型预测残差的协方差指标对系统性能进行实时监控。其中,历史性能指标用以评价系统的整体性能,协方差指标反映模型失配和干扰变化的影响。根据两个指标对不同性能影响因素的不同表现和性能恶化后对干扰新息的重新辨识结果,对系统性能下降的原因进行初步诊断,缩小性能下降源的范围,并通过Wood-berry塔实验验证了该方法的有效性。

关键词: 模型预测控制, 历史性能指标, 模型预测残差, 协方差指标, 实时监控, 实验验证