Journal
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING
Volume -, Issue -, Pages -Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/09544100231219910
Keywords
Aerial recovery; towed drogue; event-triggered control; performance constraints; trajectory control
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This paper proposes a performance event-triggered control algorithm based on disturbance observer to stabilize the flexibly towed aerial recovery drogue subject to unknown airflows, cable towing tensions, limited computation capacity, and actuating power resources. The method accurately estimates disturbances and reduces computational costs while ensuring specified transient performance. The simulation results demonstrate the effectiveness of the proposed method.
Aiming at the drogue docking control problem in aerial recovery, this paper proposes a performance event-triggered control algorithm based on disturbance observer to stabilize the flexibly towed aerial recovery drogue subject to the unknown airflows, cable towing tensions, limited computation capacity, and actuating power resources with guaranteed transient performance. Firstly, the control-oriented 6 degrees of freedom (DOF) affine nonlinear dynamics of the flexible cable-towed drogue are formulated. Then, the event-triggered extended state observers (ETESO) are established for the trajectory and attitude subsystems by utilizing intermittently measured state, to accurately estimate the lumped disturbances caused by the unmeasurable cable tension and unknown airflow disturbance while reducing the computation cost from the sensor to the observer. Moreover, to constrain the trajectory tracking error within the guaranteed transient performance constraint with any small overshoot, an ETESO-based event-triggered control algorithm for drogue was established to ensure the specified transient performance; finally, the closed-loop stability is discussed using Lyapunov analysis. The simulation results confirmed that this method can not only constrain the drogue within the expected small error range but also effectively reduce computational costs and resource occupation, with the best control effect.
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