English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Unmanned helicopters are increasingly popular platforms for unmanned aerial vehicles (UAVs). With the abilities such as hovering, taking off and landing vertically, unmanned helicopters extend the potential applications of UAVs. However, due to the complex mechanism and complicated aero-flow during flight, it is almost impossible to accurately model the dynamics of an unmanned helicopter in full flight envelope, and the significant model uncertainties associated with a nominal model may degrade the performance and even stability of an onboard controller. Due to the difficulty in obtaining a high fidelity full envelope model, the multi-mode modeling technique has been proposed for rotor aircrafts, such as tilt-rotor aircraft XV-15 [1], helicopter BO-105 [2], UH-60 [3], R-50 [4] and X-Cell [5]. The mode-dependent model, which is identified and simplified according to a specific flight mode, such as hovering, cruising, taking off and landing, can be used for control design for the corresponding flight mode. However, the mode-dependent control suffers from at least two problems: one is the difficulty in accommodating the mode transition dynamics, and the other is the compensation of the ‘model drift’ due to flight dynamics change within one particular mode. Up to now, for the purpose of practical implementation, the mode transition problem can be partially dealt with by limiting the mode switching conditions [6], e.g., mode change is made through hovering mode. Robust and adaptive control techniques [7-8], on the other hand, have been used to deal with the ‘model-shift’ within a flight mode. However, such control schemes normally need to know the boundary of internal and external uncertainties and relative noise distribution, which are difficult to identify accurately for a helicopter in full flight envelope. Although online identification technology can be used to obtain the real-time dynamics and disturbance, it is a large burden for the flight computer to reconstruct the robust controllers and reach the requested control period (>50Hz) for sampling and actuating due to the complex calculation of the robust/adaptive optimization process [9-10] and the strict weight limits of micro flight computers. Besides the model uncertainties, another critical problem that limits the control performance of a helicopter is the time delay between the actuator command and the generation of relative aerodynamic force/torque [11], which will be called aerodynamics-delay/time-delay in the following sections. Normally, this time delay may cause reduced feedback gain of a modelbased controller and result in poor robustness [12-13], i.e., sensitive to disturbances.

Predictive Control for Active Model and Its Applications on Unmanned Helicopters