This paper proposes a new method of designing a data-driven controller for aircraft maneuver. Assuming that we do not have knowledge of the controller and the controlled aircraft, we propose a controller design with explorations of the control inputs and their responses from the aircraft. Specifically, we utilize Bayesian optimization (BO) with Gaussian process (GP) regression for black-box modeling of the aircraft responses from the explored controls, which are selected as samples to experiment with BO. We tested the proposed controller with a rigid six degrees of freedom (6DoF) nonlinear aircraft model by varying the kernel structures of the GP regressions. Our proposed method shows shorter flight times and smaller deviations navigating fixed waypoints compared to the tuned Proportional Integral Derivatives (PID) controller. The proposed controller can be an alternative to PID control, particularly when both controller structure and controlled plant model information are unknown.