Trajectory-tracking hybrid controller based on ADRC and adaptive control for unmanned helicopters

Abstract

To enable unmanned helicopters to fly autonomously in precise paths and to reduce the influence of helicopter dynamic model error, this paper proposes a hybrid controller with active disturbance rejection control (ADRC) and adaptive control for trajectory tracking. This paper proposes the model reference adaptive control strategy for the inner-loop controller. This paper uses the momentum back-propagation (MOBP) neural network algorithm to tune the parameters of the proposed inner-loop controller. This paper uses ADRC in the proposed controller for velocity control. The simulation results indicate that the proposed controller can achieve good trajectory tracking. Compared with the PID controller and cascade ADRC, the proposed hybrid controller is more robust and has better anti-disturbance capability. This paper uses the proposed hybrid controller for trajectory-tracking control of the XV-2, which is an unmanned helicopter with a gross take-off weight level of 200 kg. With the help of the hybrid controller in our flight test, the root mean square error of the XV-2 trajectory-tracking control is within 0.6 m.

Keywords

• unmanned helicopter
• adaptive control
• active disturbance rejection control
• flight control
• neural network