Static gains are often required for control, diagnosis and optimization of nonlinear dynamic systems. This paper proposes a new approach to estimate static gains for nonlinear dynamic systems from steady-state values hidden in historical data. First, steady-state values of system inputs and outputs are extracted by automatically finding data segments in steady-state conditions. Second, static gains of nonlinear dynamic systems in different operating conditions are estimated via linear regression from these steady-state values. The proposed approach has two practical features: (i) estimated static gains can be verified in a convincing way, because the validness of extracted steady-state values is confirmed by visualizing data segments in steady-state conditions, and the accuracy of estimated static gains is verified by comparing the extracted steady-state values and their estimates; (ii) the proposed approach is simple to understand and implement in practice, since it only involves a linear equation between steady-state values and static gains, as well as a basic technique of linear regression. Numerical simulation and industrial application demonstrate the effectiveness of the proposed approach.

非线性动态系统的控制、诊断和优化通常需要静态增益。本文提出了一种从隐藏在历史数据中的稳态值估计非线性动态系统的静态增益的新方法。首先，通过在稳态条件下自动查找数据段来提取系统输入和输出的稳态值。其次，通过线性回归从这些稳态值估计非线性动态系统在不同操作条件下的静态增益。所提出的方法有两个实际特点：（i）估计的静态增益可以以令人信服的方式验证，因为提取的稳态值的有效性通过在稳态条件下可视化数据段来确认，通过比较提取的稳态值和它们的估计值来验证估计静态增益的准确性；(ii) 所提出的方法在实践中易于理解和实施，因为它只涉及稳态值和静态增益之间的线性方程，以及线性回归的基本技术。数值模拟和工业应用证明了该方法的有效性。