In-flight range optimization of multicopters using multivariable extremum seeking with adaptive step size
This incremental improvement is useful for applications like package delivery where payloads vary and power consumption is hard to model.
The paper tackles the problem of limited flight range in multicopters by proposing a method to optimize speed and heading for maximum range, achieving over 30% faster convergence than standard extremum seeking controllers in experiments.
Limited flight range is a common problem for multicopters. To alleviate this problem, we propose a method for finding the optimal speed and heading of a multicopter when flying a given path to achieve the longest flight range. Based on a novel multivariable extremum seeking controller with adaptive step size, the method (a) does not require any power consumption model of the vehicle, (b) can adapt to unknown disturbances, (c) can be executed online, and (d) converges faster than the standard extremum seeking controller with constant step size. We conducted indoor experiments to validate the effectiveness of this method under different payloads and initial conditions, and showed that it is able to converge more than 30% faster than the standard extremum seeking controller. This method is especially useful for applications such as package delivery, where the size and weight of the payload differ for different deliveries and the power consumption of the vehicle is hard to model.