Given the transformations and promotion of technologies and modernization in different
areas of society, such as the use of Unmanned Aerial Vehicles performing numerous automated activities, it is necessary to create algorithms with efficiency and safety to avoid
losses and damages in their functions. Aerial systems are, for the most part, multiple input
and multiple output systems, time-varying, susceptible to disturbances and measurement
noise, becoming a challenging scenario in the area of system identification. Given this, such
dynamics must be considered in the identification process. Therefore, the objective of this
work is to develop an algorithm capable of jointly estimating the states and parameters of
systems, mitigating the interference of measurement noise and external disturbances in the
real-time identification process. Based on these principles, the creation of the joint estimation algorithm Extended Kalman Filter with Instrumental Variables was established. The
proposed algorithm stands out for its theoretical commitment to minimizing interference
from dynamics that can affect the reliability of parameters calculated by identification
methods already consolidated in the literature, such as Extended Kalman Filter (EKF)
and Recursive Least Squares (RLS). The proposed method was tested to calculate the
stochastic linear model of the autopilot system of the unmanned aerial quadcopter, Parrot’s
AR Drone 2.0 model, taking into account scenarios in which the sensor signal presents
a signal-to-noise ratio of 100, 50, 10. Its performance was compared with RLS and EKF
parameter estimation. To evaluate the state estimates, the root-mean-square deviation
norm index was used and, to evaluate the parameters, the Euclidean distance between the
real parameters and the estimated parameters was used. Finally, the data collected by the
methods were used to tune the Gaussian Quadratic Linear Control controller, thus allowing
comparison of the impact of the identification method on the closed-loop behavior of the
aerial system. To enable discussion and comparison of control algorithms, the Squared
Error Integral and Squared Control Integral indices were applied to evaluate the control
performance, the gain margin and the phase margin to measure system robustness.
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