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A variable-parameter-model-based feedforward compensation method for tracking control
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AI
DOI：10.1109/JAS.2020.1003135.png)
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Abstract
En 中文
Base on the accurate inverse of a system, the feedforward compensation method can compensate the tracking error of a linear system dramatically. However, many control systems have complex dynamics and their accurate inverses are difficult to obtain. In the paper, a variable parameter model is proposed to describe a system and a multi-step adaptive seeking approach is used to obtain its parameters in real time. Based on the proposed model, a variable-parameter-model-based feedforward compensation method is proposed, and a disturbance observer is used to overcome the influence of the model uncertainty. Theoretical analysis and simulation results show that the variable-parameter-model-based feedforward compensation method can obtain better performance than the traditional feedforward compensation.
Keywords:
Disturbance observer
feedforward compensation
iterative learning control
parameter identification
system model
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