Tuning the Proportional-Integral-Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach.

Nowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional-integral-derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 × 10-5 RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments.

The Immigrant and the Citizen: Out-Group Evaluations and Well-Being of Turkish Immigrants From Bulgaria.

This study examines the postulates of the Rejection Identification Model (RIM) and Rejection Disidentification Model (RDIM) in a sample of 314 ethnic Turks from Bulgaria who migrated to Turkey. We investigate the intervening roles of immigrant and citizen identifications between perceived discrimination and the outcome variables (well-being and out-group evaluations). The results indicate that perceived discrimination predicts negative affect and out-group evaluations. Besides, Turkish citizen identification significantly and positively predicts life satisfaction and satisfaction from living in Turkey, whereas immigrant identification negatively predicts satisfaction in Turkey. Citizen identification predicts positive, and immigrant identification predicts negative out-group evaluations. Immigrant identification plays a mediating role in the link between perceived discrimination and satisfaction in Turkey as well as in that between perceived discrimination and out-group attitudes. The results imply the importance of consideration of contextual factors, including historical and cultural backgrounds, and the meaning of different identities for minority groups in predicting well-being.