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 the paper. 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 (DESO) 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 I/O data is given to make a better performance and a more convenient application compared with the typical MFAC scheme. Further, 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.

The spatial disturbance, which depends on position rather than time, is widespread in rotary machines. In this article, an improved control method named accurate cycle aligned repetitive control (ACARC) is proposed to deal with its suppression. In the ACARC, the data-storage technique is used to realize the spatial internal model and a design requirement is presented for the rejection of spatial disturbance with a better response. Further, the cycle offset, caused by the auxiliary stabilizing compensator, is significantly reduced by the combination of a spatial low-pass compensator and a time-advance element. Moreover, a cascaded structure of ACARCs is presented together with a detailed design procedure for disturbance with multiple components occurring in actual rotary systems. The effectiveness and superiority of the proposed control method are verified and compared with other methods by the simulations and experiments.

Spatially cyclic disturbances exist widely in rotating machines. They usually have fixed spatial cycles rather than constant time periods, which affect the stationarity of angular speed tracking. An input matching least mean square (LMS) adaptive filter (IMLMS-AF) is proposed to cope with the effects of spatially cyclic disturbances. The IMLMS-AF sets a part of the inputs as spatially cyclic signals for disturbance rejection and forms another part as a time-dependent function for reference tracking. Furthermore, an updated law and convergence of the pending weights are given. The system's stability is proved by combining the instantaneous gradient with Lyapunov theory. Moreover, the IMLMS-AFs are parallelized to reject disturbance with multiple components and reference tracking. The effectiveness and superiority of the proposed control method are verified and compared with other methods by simulations and experiments.