The model-plant mismatch (MPM) can be responsible for poor control performance. This can be solved by locating which channels (i.e., pars of MVs-CVs) are suffering the highest MPM, and then, proceed with the model updating using the same historical data used for the assessment and diagnosis steps. This paper proposes a method for updating models based on the nominal error: a benchmark that quantifies the model discrepancy by comparing the measured output of a system with its corresponding nominal output, i.e., the output of the closed-loop with no MPM or unmeasured disturbances. The main advantages of the method are to avoid usual multivariable model identification and use simpler SISO structures, thus reducing the workload of the model maintenance. The effectiveness of the method is illustrated using the quadruple-tank process with a nonminimum phase operating point to explore multivariable characteristics of the final updated model. All the required methodologies for assessment, diagnosis, and model maintenance are also presented in the paper and can be applied in the same historical data. No additional plant perturbations are required to improve the model. Although it is not limited to Model Predictive Control (MPC), the proposed methodology can be successfully applied to MPC assessment, diagnosis, and model maintenance.

模型工厂不匹配（MPM）可能导致不良的控制性能。这可以通过定位哪些通道（即MVs-CV的参数）遭受最高的MPM来解决，然后使用与评估和诊断步骤相同的历史数据进行模型更新。本文提出了一种基于标称误差的模型更新方法：一种基准，通过比较系统的测量输出与其对应的标称输出（即无MPM或无测量干扰的闭环输出）来量化模型差异。 。该方法的主要优点是避免通常的多变量模型识别，并使用更简单的SISO结构，从而减少了模型维护的工作量。使用具有非最小相位工作点的四缸工艺来说明最终更新模型的多变量特征，说明了该方法的有效性。本文还介绍了评估，诊断和模型维护所需的所有方法，并且可以将它们应用在相同的历史数据中。无需其他植物干扰即可改善模型。尽管不限于模型预测控制（MPC），但所提出的方法可以成功地应用于MPC评估，诊断和模型维护。不需要其他植物扰动来改善模型。尽管不限于模型预测控制（MPC），但所提出的方法可以成功地应用于MPC评估，诊断和模型维护。不需要其他植物扰动来改善模型。尽管不限于模型预测控制（MPC），但所提出的方法可以成功地应用于MPC评估，诊断和模型维护。