Data-Driven Model-Free Adaptive Attitude Control Approach for Launch Vehicle With Virtual Reference Feedback Parameters Dining Method

Modeling a launch vehicle dynamics accurately is time-consuming since its dynamics is very complex with high nonlinearity, when considering the influence of load variation and other related factors. Model-free adaptive control (MFAC), as a data-driven control method, has been widely used because of its simple controller structure, low-computational burden, and easy implementation. In this paper, a data-driven attitude improved model-free adaptive control (iMFAC) is first applied for a launch vehicle. First, a controller is designed for the launch vehicle by utilizing the MFAC. Then, the initial values of the pseudo gradient (PG) and the reset values of the PG in the designed controller are optimized under the virtual reference feedback tuning (VRFT) framework through the equivalent relationship between the MFAC and the VRFT in controller structure. Finally, the effectiveness and robustness of the applied iMFAC are verified through qualitative and quantitative analysis compared to the MFAC and PID.