Wind estimation for lateral path following of UAVs using higher order sliding mode

Abstract

In design of guidance algorithm's wind is often ignored or only considered implicitly. Persistent winds has a very significant nonlinear effect on the guidance scheme as for small UAVs these disturbances can strongly affect their spatial orientation. This research work extends the idea of sliding mode control for parameter estimation of UAV nonlinear dynamics. The uncertain parameter estimation scheme is designed to estimate wind based on the higher order sliding mode robust differentia tor (HOSMD) using rate of change of heading of the vehicle. Further these estimates are then included in the guidance algorithm. The UAVs guidance algorithm's objective is to derive the lateral track error towards zero with graceful and stable manoeuvres and then to keep it as minimum as possible while subject to disturbing winds. In this scheme a second order sliding motion is established along designed sliding manifold and outputs the reference bank commands for improved tracking performance using estimates during curved arcs. The estimation is combined seamlessly with robust guidance algorithm to produce integrated identification and guidance scheme for lateral path following application. The combined framework is capable of robust accurate path following in the presence of wind disturbance. The algorithm is implemented in the flight control simulation of scaled YAK-54 research UAV; simulation test results are presented. These results demonstrate the effectiveness and performance of the proposed lateral guidance scheme.

Communication-Aware UAV Path Planning

“…For instance, in [39], the authors dealt with the wind disturbances on trajectory planning by iteratively solving a no wind case problem with a moving virtual target. In most of the work like in [40]- [42], the authors usually compensate the wind in the control loop design of the UAVs to keep the position error of flight path as minimum as possible with respect to the desired path. However, we are compensating for the wind disturbances during the path computation, before the flight.…”

Using a quadrotor as wind sensor: time-varying parameter estimation algorithms

“…Boiko & Chehadeh (2018) used a sliding mode differentiator to estimate the quadrotor velocity with fusion of the position sensor from the motion capture optical system and accelerometer. Ali, Shah, Samar, & Waseem (2016) used the high order sliding mode differentiator to estimate the wind velocity for a fixed-wing UAV, using the rate of change of heading of the vehicle. Other works that use IMU and motion tracking system were carried out by Neumann & Bartholmai (2015); Rhudy et al (2015); Sikkel, Croon, Wagter, & Chu (2016); Song et al (2016); Waslander & Wang (2009); Xiang et al (2016).…”