System Identification Method for Robust Control

Abstract

The invention provides a system identification method for robust control, which belongs to the field of control and is used for determining the range of values ​​of nonlinear system parameters, including: establishing a discrete switching model equation, simplifying the discrete switching model, and obtaining the equation The simple expression of the model; obtain the model input and output data, and substitute the model data into the simple expression to solve the equation parameter data set; filter the data in the parameter data set to obtain the filtered data, and redistribute the filtered data to obtain Redistribute parameter dataset. In the process of determining the parameter data set, based on the idea of ​​the maximum feasible set, and at the same time adopting an efficient approximate calculation method, the upper and lower limits of the parameters of the time-varying system can be quickly identified, which makes up for the shortcomings of various identification methods in the prior art , which can improve the efficiency of determining the value range of parameters.

Description

technical field [0001] The invention belongs to the field of control, and particularly relates to a system identification method for robust control. Background technique [0002] In the design process of the controller, the controlled object is usually described as a linear system or a nonlinear system is expanded near a certain operating point and then approximated into a linear system, and then a linear controller is introduced on this basis. [0003] In the chemical industry, the characteristics of the controlled object (such as flow, temperature, pressure, etc.) will change with time under many working conditions (this type of system is also called a time-varying system) or the controlled object has strong nonlinearity. . Therefore, the parameters of the approximated linear system do not represent the actual system well. That is, the parameters of the actual system are uncertain; or there is a certain deviation between the model and the actual system. To ensure smooth...

AI Technical Summary

Problems solved by technology

[0004] In the existing technology, robust control methods are relatively mature, but the identification methods for uncertain systems are very limited; at the same time, most robust control methods are based on the assumption that the uncertainty of the controlled system model is known, But this does not conform to the actual situation and cannot meet the needs of the actual control process

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