Data-driven dynamic internal model control technology

Abstract

The invention discloses a data-driven dynamic internal model control technology, and belongs to the field of intelligent control. Under a data-driven framework, aiming at the problem that the non-linear non-affine system needs to improve the processing capability for uncertainty and disturbance, the control scheme provided by the invention comprises the following steps of: converting the unknown non-linear non-affine system into an equivalent linear data model by using a dynamic linearization method, and regarding the linear data model as an internal model; designing a parameter updating law to estimate unknown pseudo partial derivative parameters in the linear data model; designing a nominal controller capable of enabling the system to output a tracking target trajectory; and designing an uncertainty compensation controller to compensate disturbance and mismatch between an actual system and the internal model. The data-driven dynamic internal model control technology disclosed by the invention is simple and convenient in implementation mode, small in operand, convenient for engineering implementation, and capable of effectively inhibiting disturbance and realizing a relatively good tracking effect under the condition that only input and output data can be obtained.

Description

[0001] The invention belongs to the technical field of intelligent control, and more specifically relates to a data-driven dynamic internal model control technology. Background technique [0002] The internal model control method has the function of suppressing disturbance and model mismatch, and can effectively improve the robustness of the system. The main idea of ​​internal model control is to construct a model of the controlled system, called the internal model, and obtain the inverse controller by inverting the internal model, and the inverse controller generates the system output through the difference between the system output and the target output. The output tracks the control amount of the target output. If the internal model and the system model match accurately, the tracking effect and the disturbance suppression effect will be ideal. Therefore, for the internal model control system, the key problem is to obtain the system model and the inverse of the system model....

AI Technical Summary

Problems solved by technology

For nonlinear systems, there are currently some model-based internal model control methods. However, as the system becomes more and more complex, it becomes more and more difficult to obtain the system mechanism model. To solve this problem, some scholars propose to use neural networks or fuzzy systems to control However, these methods require a large amount of data and calculations, and sometimes online training is required to ensure the accuracy of the model, and the structure of the neural network or fuzzy model is based on different systems. vary

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