Patent Document ID: 9864356
Application ID: 14398904
Patent Flag: 1

Claim One:
1. Identification method of nonlinear parameter varying models (NPV), the method based on nonlinear model identification system, said system comprises four parts including input module, identified object, output module, and system identification module, the input module applies the excitation signal to the identified object, the output signal of the identified object is sent to the system identification module by using the output module, the operating conditions of the identified object are described with one or more operating point variables, said method comprises the following steps: Step (1), local nonlinear model tests: the term “local” refers to that the value of the operating point variable remains constant; firstly, set the value of the operating point variable; on the premise of that the operating point variable remains constant, set the values of the manipulating variables in their working intervals; then, traverse all the values of the manipulating variables (MV) in their working intervals by using the input module, and obtain the steady-state output data with respect to one controlled variable (CV) from the output module; if the number of the manipulating variables (MV) is more than one, when traversing all the values of one manipulating variable in its working interval by using the input module, other manipulating variables remain constant; finally, the excitation signal is applied to all the manipulating variables (MV) of the identified object by using the input module, and obtain the dynamic output data with respect to said controlled variable (CV) from the output module; Step (2), identify local nonlinear models: the system identification module automatically carries out the local nonlinear model identification based on the steady output data and dynamic output data obtained in step (1) by using the nonlinear identification method to estimate the parameters of the local nonlinear models, and then obtains the local nonlinear models at the set value of the operating point variable in step (1); Step (3), repeat the above step (1) and step (2) for automatically completing the identification of all required local nonlinear models with respect to all set operating point variable values, and obtain all required local nonlinear models of all set operating point variable values; said all set operating point variable values include the upper bound and the lower bound of the operating range of the operating point variable; and then check whether the local nonlinear models can meet the required accuracy threshold; if it is not satisfied, adjust the values of the manipulating variables in their working intervals and/or adjust the excitation signal, then return to step (1); otherwise, continue; Step (4), operating point variable transition tests: said operating point variable transition tests are to transit the identified object from one operating point variable value to another operating point variable value by using the input module; in the operating point variable transition tests, change the operating point variable value, and apply the excitation signal to all the manipulating variables (MV) of the identified object by using the input module, and then obtain the operating point variable transition dynamic output data with respect to said controlled variable (CV) from the output module; Step (5), identify the multi-input single-output nonlinear parameter varying model: all required local nonlinear models are utilized to convert the local nonlinear models to the multi-input single-output nonlinear parameter varying model (NPV) by using the interpolation philosophy, the weighting functions used in the interpolation philosophy are determined by the parameter estimation method based on the total testing data which include the local nonlinear model test data and the operating point variable transition test data; Step (6), check whether the multi-input single-output nonlinear parameter varying model (NPV) obtained in Step (5) can meet the required accuracy threshold; if it is not satisfied, increase the number of the local nonlinear models and turn to step (3), or increase the operating point variable transition test data and turn to step (4); if satisfied, continue; Step (7), repeat the above steps from step (1) to step (6) for identifying all the multi-input single-output nonlinear parameter varying models (NPV) with respect to all the controlled variables (CV), and then complete the identification of the multi-input multi-output nonlinear parameter varying models (NPV) by putting together all the multi-input single-output nonlinear parameter varying models (NPV) with respect to all the controlled variables (CV).