Paper Title
STATE AND PARAMETER ESTIMATION ARCHITECTURE FOR QUAD-ROTOR TAIL-SITTER VTOL UAV

Abstract
This paper presents a novel architecture for the simultaneous state and parameter estimation of a quad-rotor tail-sitter Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV). Tail-sitter VTOL UAVs, known for their hybrid capabilities between hovering and forward flight, pose significant challenges in control and navigation due to their complex dynamics. To address these challenges, our framework integrates multiplier matrices, a data-driven concurrent learning strategy, and an adaptive observer. This combination enables improved performance in state and parameter estimation, providing robustness where traditional methods may falter. A critical feature of the proposed architecture is its ability to guarantee the convergence of parameter estimation error under specific excitation conditions. By ensuring these conditions are met during flight, the estimation error converges to a defined neighborhood around the origin, enhancing the precision of the UAV's control system. This characteristic is essential for real-time adaptability in dynamic environments, where rapid adjustments are necessary to maintain stability. Our solution represents a significant advancement for hybrid VTOL UAVs, particularly in ensuring smooth transitions between different flight modes. Keywords - State Estimation, Parameter Estimation, Quad-Rotor Tail-Sitter VTOL UAV, Extended Luenberger, Nonlinear Observers