In this paper, the performance of Model-Free Adaptive Control (MFAC) has been investigated on a novel and specific moving mass controlled (MMC) flying robot system. The novel one-degree-of-freedom (1 DOF) MMC flying robot test bed presented
in this paper has highly nonlinear and slow dynamics with a variable center of gravity (CoG) and moment of inertia. This
makes the control of this system a challenging problem. One of the solutions to this challenge is the use of data-driven control
methods, in particular, MFAC. This controller uses a data-driven model to control the system using only input and output
(I/O) data. This paper compares this data-driven controller with proportional-integral-derivative (PID) control, and Linear
Quadratic Regulator (LQR) as two model-free and model-based controllers which are widely used controllers in industry.
The results of the comparison show that in the various scenarios applied, MFAC has a clear superiority over the PID and
LQR, and its adaptive structure gives more freedom of action in the implementation of different scenarios and the presented
noise. The results are obtained using the Integral Time Absolute Error (ITAE) criteria and the mean maximum error has
also been compared in a Monte Carlo analysis. For a more detailed study, the amount of control energy consumption was
also compared, which showed a clear superiority of the MFAC. Also, the robustness of the controller was demonstrated by
introducing uncertainty in the plant parameters and by running 100 Monte Carlo simulations with random initial conditions.
Finally, despite the PID controller, the MFAC followed the desired scenarios well and compared to LQR consumed less
energy. The results demonstrate that the MFAC outperformed the PID and LQR controllers in the presence of random initial
conditions and noise in terms of mean maximum error (70.4%), mean ITAE (91%), and energy consumption (46%).