  This paper presents a new framework to use images as the inputs for the
controller to have autonomous flight, considering the noisy indoor environment
and uncertainties. A new Proportional-Integral-Derivative-Accelerated (PIDA)
control with a derivative filter is proposed to improves drone/quadcopter
flight stability within a noisy environment and enables autonomous flight using
object and depth detection techniques. The mathematical model is derived from
an accurate model with a high level of fidelity by addressing the problems of
non-linearity, uncertainties, and coupling. The proposed PIDA controller is
tuned by Stochastic Dual Simplex Algorithm (SDSA) to support autonomous flight.
The simulation results show that adapting the deep learning-based image
understanding techniques (RetinaNet ant colony detection and PSMNet) to the
proposed controller can enable the generation and tracking of the desired point
in the presence of environmental disturbances.
