Quad-rotor is very suitable for payload transportation due to the merits of high maneuverability and free hovering. However, the unknown varying payloads can cause negative influences that act in forms of persistent disturbances and sudden changes, damaging flight performance especially the attitude stability seriously. Targeting the persistent disturbances, an entirely novel disturbance estimator (DE) which can estimate non-smooth disturbances in a highly accurate manner for feedback compensation is proposed in this paper. To deal with the sudden changes from prescribed references and the payloads that may induce too large overshoots and input surging, a type of predictive optimal controller, which considers tracking errors and their changing rates of a class of linear multiple-input-multiple-output systems, is developed. Simulation results show that the system enhanced by the DE has better control performance than the ones enhanced by the commonly used extended state observer or nonlinear disturbance observer. Compared with the typical control approaches, the proposed control scheme enables the quad-rotor attitude system more stable performance and more ideal inputs on both persistent disturbance and sudden change resisting during payload transportation. INDEX TERMS Quad-rotor, payload transportation, disturbance attenuation, predictive optimal control.

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Nowadays, manipulation of quad-rotors faces complexity in controller parameter tuning process and system instability under uncertainties. Internal model control is featured with less controller parameters, simpler tuning process than conventional methods, good robustness and perfect capability in rejection of uncertainties. All its merits can be applied in the field of nano-quad-rotor control since its internal model is easy to be obtained and the suffered uncertainties, especially persistent ones such as model uncertainties and winds, can be rejected by the algorithm effectively. In this paper, an internal model control cascade Proportion-Integration-Differentiation (PID) method is developed to enhance the robustness and improve the capability of uncertainty rejection of nano-quad-rotors flying under persistent uncertainties. The system can be stabilized in a very easy way with all controller parameters tuned within 0 to 1. Comparison with internal model control method was carried out numerically; the results show that, in dealing with persistent uncertainties, the internal model control cascade PID-based method presents significant superiority in the maintenance of both the accuracy of trajectory tracking and the stability of attitude.

This paper investigates the landing planar movement control (LPMC) problem of an amphibious airplane which is susceptible to uncertainties such as the gust, the unmodeled dynamics and the strong couplings. The uncertainties can bring negative effects for the airplane acting in forms of persistent influences and sudden changes, damaging system stability and causing rollover. To attenuate the persistent disturbances, an entirely novel disturbance estimator that can estimate the non-smooth disturbances accurately is designed. To degrade the impacts from sudden changes, a type of predictive controller is developed such that input surging can be suppressed. Comparison with the conventional PID method shows that the proposed approach enables the system good robustness in attenuating both persistent disturbances and sudden changes during the LPMC.

This paper proposes a novel model-free adaptive predictive control approach with low online computational load for of a class of unknown discretetime nonlinear multiple-input multiple-output systems. First, historical measurable input/output data is utilized to online construct equivalent explicit data models for control design, making the controllers model-free. Second, input structuralization where system inputs are expressed by linear combination of a few basis functions is integrated into predictive controller design for reducing online computational load. Third, a novel Kalman Filter-based regression factor computation approach is developed to predict data model parameters for realizing predictive function of controllers such that closed-loop system robustness can be enhanced when sudden changes from prescribed references are encountered. Last, numerical simulations validate that, by using much less online computational load, the proposed approach can achieve equivalent control performance compared with the existing approaches.