PID-Like Model Free Adaptive Control With Discrete Extended State Observer and Its Application on an Unmanned Helicopter

Aiming at the problem that how to design a controller without using model information for an unmanned helicopter (UH), a novel data-driven method based on model free adaptive control (MFAC) is proposed in this article. A new form of dynamic linearization (DL) equation composed of pseudo-partial derivative (PPD) and external disturbance is built in order to reduce the influence of disturbance on PPD. Since there are internal and external unknowns included in the DL equation, a nominal DL equation without external disturbance item is introduced to make the estimation of the two items decoupled. The estimation algorithm of PPD is designed based on the nominal DL equation, and a discrete extended state observer is applied specifically into the estimation of external disturbance based on the original DL equation. Besides, with the nominal DL equation, a modified MFAC scheme called PID-like MFAC, which considers the historical input and output (I/O) data is given to make a better performance and a more convenient application compared with the typical MFAC scheme. Furthermore, the stability of the control scheme is proved. Finally, comparative simulations are carried out to verify the effectiveness of the proposed control scheme. An experiment based on UH also verifies its practical performance and the theoretical findings.