Due to the model-plant mismatch in practical applications of a nonlinear model, it is necessary to estimate both states and model parameters online. However, when the number of uncertain parameters is large, it is difficult to estimate all the parameters due to a lack of information in measurements. Under this condition, model prediction can be inaccurate although the current states are accurately estimated. To improve the accuracy of both state estimation and prediction, we propose a moving horizon estimation combined with a parameter subset selection scheme. In the proposed MHE framework, a subset of estimable parameters is selected within each horizon. Then, only the selected parameters are estimated along with the state variables. The proposed method is illustrated with the numerical example of a fed-batch bioreactor. The result shows that the proposed method improves the accuracy of model prediction, compared to the conventional MHE, while maintaining the state estimation performance.

由于在非线性模型的实际应用中模型工厂不匹配，因此必须在线估计状态和模型参数。然而，当不确定参数的数量很大时，由于测量中缺乏信息，难以估计所有参数。在这种情况下，尽管可以准确估计当前状态，但模型预测可能会不准确。为了提高状态估计和预测的准确性，我们提出了结合参数子集选择方案的运动层估计。在提出的MHE框架中，在每个范围内选择可估计参数的子集。然后，仅估计选择的参数以及状态变量。用分批补料生物反应器的数值实例说明了所提出的方法。