Vision-Based Control of an Unknown Suspended Payload with a Multirotor

Abstract

This paper presents a vision-based control strategy for a rotary-wing unmanned aerial vehicle (RUAV) transporting an unknown suspended payload. The suspended payload parameters, which include its mass and cable length, are unknown and direct measurements of its states are not available. A feedforward-feedback adaptive control strategy, that consists of a notch filter and linear quadratic Gaussian (LQG) controller, is proposed to simultaneously avoid the excitation and actively damp the payload swing oscillations. The unknown payload mass is estimated using recursive least squares and the unknown cable length is estimated using a dedicated sine wave estimator. The payload parameter estimates are then used to adapt the control strategy for the specific suspended payload. A vision-based state estimator is implemented to provide payload state estimates for the optimal full-state feedback controller. Simulation results show that the control strategy successfully adapts for different suspended payloads and effectively damps the unwanted payload swing oscillations.