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In the paper, a family of novel real-time tuning methods for an unmanned aerial vehicle (UAV) altitude controller in in-flight conditions. The methods allow the controller’s gains to be adapted only on the basis of measurements from a basic sensory equipment and by constructing the optimization cost function in an on-line fashion with virtually no impeding computational complexity; in the case of the altitude controller as in this paper for a hexacopter, altitude measurements were used only. The methods are not dependent on the measurement level, and present the approach in a generally applicable form to tuning arbitrary controllers with low number of parameters. Real-world experimental flights, preceded by simulation tests, have shown which method should behave best in a noisy environment when e.g. wind disturbances act on a UAV while it is in autonomous flight. As the methods can potentially be extended to other control loops or controller types, making this a versatile, rapid-tuning tool. It has been shown that a well-tuned controller using the proposed AL-TUNE scheme outperforms controllers that are tuned just to stabilize the system. AL-TUNE provides a new way of using UAVs in terms of adaptivity to changing their dynamic properties and can be deployed rapidly. This enables new applications and extends the usability of fully autonomous UAVs, unlike other tuning methods, which basically require the availability of a UAV model. The core difference with respect to other research from the field is that other authors either use a model of a UAV to optimize the gains analytically or use machine learning techniques, what increases time consumption, whereas the presented methods offer a rapid way to tune controllers, in a reliable way, with deterministic time requirements.

Autonomous estimation of the state is of key importance in UAVs, as the measurement systems may experience faults and failures. Thus estimation techniques must provide estimates of the most important variables used in the control algorithms for safe, autonomous, unmanned flights. In this paper, a filter with low computational complexity for attitude estimation of a quadrotor UAV is introduced, with a model suitable for Fault-Tolerant Observation. The new filtration method, called the Square Root Unscented Complementary Kalman Filter (SRUCKF), is based on the commonly-known Kalman Filter (KF) in its nonlinear version, namely the Square Root Unscented Kalman Filter (SRUKF). The fundamental equation of the KF is modified so that the complementary feature of the filter is exalted. The new filter introduces characteristics that are analyzed on the basis of its application in quadrotor state estimation. Finally, the results are compared to an ordinary filter of the same type (using the Unscented Transformation). The presented studies indicate that the newly derived filter (SRUCKF) handles strong nonlinearities and gives results similar to those obtained from the SRUKF. Furthermore, it introduces lower computational burden, as the undergoing process uses diagonal matrices in its crucial places. In the paper, the estimation algorithms are tailored to a quadrotor UAV (Crazyflie 2.0), for which a quaternion-based model is proposed. The contribution of the paper lies in a Kalman-based novel state observer and its application in Fault-Tolerant Observation (FTO).

The parametrized model of the Unmanned Aerial Vehicle (UAV) is a crucial part of control algorithms, estimation processes and fault diagnostic systems. Among plenty of available methods for model structure or model parameters estimation, there are a few, which are suitable for nonlinear UAV models. In this work authors propose an estimation method of parameters of the coaxial quadrotor’s orientation model, based on the Square Root Unscented Kalman Filter (SRUKF). The model structure with different aerodynamic aspects is presented. The model is enhanced with various friction types, so it reflects the real quadrotor characteristics more precisely. In order to validate the estimation method, the experiments are conducted in a special hall and essential data is gathered. The research shows that the SRUKF, can provide fast and reliable estimation of the model parameters, however the classic method may lead to serious instabilities. Necessary modifications of the estimation algorithm are included, so the approach is more robust in terms of numerical stability. The resultant method allows for dynamics of selected parameters to be changed and is proved to be adequate for on-line estimation. The studies reveals tracking properties of the algorithm, which makes the method more viable.