This study introduces an adaptive restricted controller designed to address the path-tracking challenge encountered by an autonomous quadrotor aerial vehicle navigating through confined spaces. The quadrotor dynamics are represented mathematically using quaternions, facilitating the development of the adaptive control strategy. The proposed controller incorporates an adaptive state-dependent gain to regulate the quadrotor's motion within permissible airspace. Utilizing a gain auto-tuning approach, the controller ensures convergence of the quadrotor trajectory to a predefined reference path, effectively managing both the position and orientation of the vehicle under quaternion dynamics. The control design integrates a direct barrier dead-zone controlled Lyapunov function, explicitly considering state restrictions. This function validates convergence to the reference path and derives the necessary state-dependent gains for the adaptive controller. Numerical evaluations demonstrate the efficacy of the proposed adaptive controller in achieving superior tracking performance compared to conventional state feedback controllers. Furthermore, the controller ensures compliance with full-state constraints, offering a promising solution for real-world applications. Experimental validation in a setup with predefined state restrictions further confirms the effectiveness of the proposed approach, exhibiting improved tracking characteristics over the proprietary controller installed in the quadrotor.

Reforestation is pivotal in mitigating climate change, preserving biodiversity, and safeguarding ecosystems. Precision reforestation implies efficiently using materials and human resources to conduct this intensive task. In this sense, using unmanned aerial vehicles (UAVs), also known as drones, improves the accuracy of dispensed seeds while increasing coverage and reducing labor. However, drone-based reforestation still presents technological challenges that need to be addressed. An important challenge is the presence of disturbances during flights due to environmental conditions, primarily unexpected wind, and the unavoidable loss of mass presented by the vehicle caused by the dispensing task. This paper evaluates the use of an observer-based controller to reject the particular disturbances caused by the sowing activity. Through meticulous experimentation and analysis, the study demonstrates the observer’s adeptness in mitigating external disturbances, thereby enhancing the precision and stability of UAV operations. This technological advancement holds promise for diverse practical applications and has implications for environmental conservation efforts, particularly reforestation. The obtained experimental results confirm the viability of the proposed controller and observer framework, highlighting its potential to improve the robustness of environmental monitoring, conservation, and sustainable resource management practices.