Dynamic Linear Predictive Optimization of Flexible Robot Profiling MFA Model

Abstract EN

As the airbag of a flexible robot is affected by external environmental factors during the profiling process, there are many uncertainties in the process of deformation of the airbag.

For this reason, the general nonlinear control strategy cannot obtain an accurate data model.

In this paper, a flexible robot profiling MFA (Model-Free Adaptive) model based on adaptive predictive dynamic linear optimization is proposed.

Firstly, the real-time thickness of the airbag is obtained through edge detection by using the image processing algorithm.

Secondly, the airbag aerodynamic model is constructed by visual servo control strategy.

Then, a nonlinear control system based on model-free adaptive control is established.

Thirdly, the weighting factor is used to limit the variation range of the input quantity, and the deviation of the actual value and the expected value is corrected by the adaptive prediction mechanism.

Finally, the servo control the airbag is completed.

The experimental results show that the improved model proposed in this paper solves the overshoot phenomenon of the standard control model with less control error and higher robustness.