Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning, which can significantly compromise control and reduce autopilot effectiveness. This paper presents a novel control strategy to improve the controllability of unmanned aircraft in challenging wind conditions. First, the equations of motion for the aircraft are reformulated as a system of stochastic differential equations, which are subsequently transformed into a deterministic form. By modeling turbulence as a Gaussian random process and incorporating it directly into the control system, the proposed method proactively compensates for the adverse effects of turbulence. The transformation is achieved using semi-invariant techniques. Second, the control problem is formulated as an optimization task, aiming to minimize the deviation between the actual and desired turn characteristics, specifically the angular velocity. Finally, a new numerical method with proven global convergence is employed to compute the optimal autopilot parameters. Simulation results using a medium-range unmanned aircraft model under continuous turbulent gusts demonstrate that the proposed method significantly outperforms existing approaches, ensuring both stability and precision in turbulent wind conditions.