Search

We introduce an algebraically active disturbance rejection-based control solution for the trajectory tracking problem of an uncertain second-order flat system with unknown external disturbances. To this end, we first algebraically identify the system’s unknown dynamics and the external disturbances with a linear set of time-varying integral expressions for the output and the control signal. We use the identified dynamics on an online feedback cancellation scheme to linearize the second-order system and cancel the uncertainties. With a proportional-integral controller we stabilize the linearized system without the need to estimate the velocity and have feedback from it. We carry out the stability analysis using linear systems theory. Finally, we evaluate the effectiveness of the proposed controller in a partially known 2-DOF manipulator.

In a world with great challenges in food security, optimising cereal production is critical. Cereals are the most important food source for human consumption. The fourth important cereal worldwide after wheat, rice and maize, is Barley, and its production strongly depends on fertilization treatments. The adoption of suitable fertilizer management strategies often results in large economic benefits to producers. However, determining optimal fertilizer doses for a specific barley variety is complex. The collection of data and their analyses can be cost prohibitive for small farmers regarding time and money. This paper introduces an approach to support producers with automatic tools for the analysis of fertilization management of barley. The proposed methodology aims to simultaneously estimate nitrogen fertilization and barley yield, from information derived from aerial RGB images captured by a UAV. Our long term goal is to provide a low-cost and wide-are-coverage solution for the estimation of barley variables that can be leveraged to increase barley yield without increasing costs. A low-cost UAV is used to capture RGB crop field images. Then, a deep convolutional neural network is used for the automated extraction of features from the images. Extracted features are feed into predictive models that estimate the variables of interest. Experimental results reveal that the proposed methodology is able to reach an accuracy above 83% when estimating nitrogen fertilization and a high correlation and low RMSE in the estimation of yield in grams. Experimental results are promising and will pave the way for the development of deep learning methods for barley analysis from aerial imagery that can be accessed by the average farmer. [ABSTRACT FROM AUTHOR]