In this work, a nonlinear estimator-based robust controller is designed for the position and yaw control of a quadrotor with uncertainty estimation. This controller ensures the tracking of desired references in the presence of parameters variation and external disturbances, making use of high-order sliding mode (HOSM) estimators to estimate these perturbations that can be canceled by the control, thus improving the dynamic behavior of the controlled system. Its performance is evaluated making use of a Simcenter Amesim quadrotor based on physical models generated from experimental data in a co-simulation framework with Matlab–Simulink used to implement the designed controller with FPGA implementation. A challenging and generic maneuver with time-varying wind disturbances and uncertainty model parameters is considered.

In this paper we deal with the problem of stabilizing a nonlinear system with state delays by means of quantized sampled-data state feedback control laws. Quantization in the state measurements and in the input signal are simultaneously considered. Fully nonlinear (i.e., possibly non-affine in the control) time-delay systems are studied. Sufficient conditions are provided such that suitably fast sampling and accurate quantization of the state feedback at hand yield semi-global practical stability, with arbitrarily small final target ball of the origin. Nonlinear delay-free systems are addressed as a special case: it is shown that the above sufficient conditions, ensuring the semi-global practical stability, are satisfied if the continuous-time static state feedback controller is a global stabilizer. The theory of stabilization in the sample-and-hold sense is used. The theoretical results are validated through an example.

The dynamic nature of quadrotor flight introduces significant uncertainty in system parameters, such as thrust and drag factors. Consequently, operators grapple with escalating challenges in implementing real-time control actions. This study presents an approach for estimating the dynamic model of Unmanned Aerial Vehicles based on Physics-Informed Neural Networks (PINNs), which is of paramount importance due to the presence of uncertain data and since control actions are required in very short computation times. In this regard, by including physical laws into neural networks, PINNs offer the potential to tackle several issues, such as heightened non-linearities in low-inertia systems, elevated measurement noise, and constraints on data availability or uncertainties, while ensuring the robustness of the solution, thus ensuring effective results in short time, once the network training has been performed and without the need to be retrained. The effectiveness of the proposed method is showcased in a simulation environment with real data and juxtaposed with a state-of-the-art technique, such as the Extended Kalman Filter (EKF). The results show that the proposed estimator outperforms the EKF both in terms of the efficacy of the solution and computation time.

In this paper, the stability preservation problem for locally Lipschitz nonlinear time--delay systems, by emulation of continuous--time dynamic output feedback controllers, is studied. Sufficient Lyapunov--like conditions are provided such that, by suitably fast sampling, the Euler emulation of continuous--time dynamic output feedback controllers ensures the semiglobal exponential stability of the related sampled--data closed--loop system. If other emulation schemes than the Euler one are used, practical semiglobal exponential stability is guaranteed, with arbitrarily small final target ball, by suitably fast sampling. The intersampling system behavior as well as time--varying sampling intervals are taken into account. In the provided results, the delay--free case is included as special case. The practical applicability of the provided results is shown through the study of a nonlinear chemical reactor system with recycle and a glucose-insulin system.