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.

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.

Inspired by the limited battery life of multi-rotor unmanned aerial vehicles (UAVs), this research investigated hierarchical real-time control of UAVs with the generation of energy-optimal reference trajectories. The goal was to design a reference generator and controller based on optimal-control theory that would guarantee energy consumption close to optimal with lower computational cost. First, a least-squares-estimation-(LSE) algorithm identified the parameters of the UAV mathematical model. Then, by considering a precise electrical model for the brushless DC motors and rest-to-rest maneuvers, the extraction of clear rules to compute the optimal mission time and generate ’energetic trajectories’ was performed. These rules emerged from analyzing the optimal-control strategy results that minimized the consumption over many simulations. Afterward, a hierarchical controller tracked those desired energetic trajectories identified as sub-optimal. Numerical experiments compared the results regarding trajectory tracking, energy performance index, and battery state of charge (SOC). A co-simulation framework consisting of commercial software tools, Simcenter Amesim for the physical modeling of the UAV, and Matlab-Simulink executed numerical simulations of the implemented controller.

Attitude control systems for ground vehicles have been an important topic in automotive research for decades, and have been extensively studied by resorting to classical continuous-time nonlinear design. Although this approach can incorporate saturation constraints and actuator dynamics in the design, the computed control laws are often approximated and applied within digital environments in absence of formal performance guarantees. In this letter, we present a quantized sampled-data approach to the vehicle attitude control problem. Starting from classical nonlinear design achieving tracking of prescribed trajectories in continuous time (emulation approach), we derive conditions for preserving the practical stability of the error dynamics by means of quantized sampled-data event-based controllers. Simulations performed in an non-ideal setting confirm the potential of the approach.

In this paper, the robust quantized sampled-data tracking control problem of nonlinear systems affected by actuation disturbances and observation errors is studied. In particular, results concerning the robustification and the quantized sampled-data implementation of tracking controllers are provided for nonlinear systems in control-affine form. Inspired by the well-known notion of Steepest Descent Feedbacks, the novel notion of Steepest Tracking Feedback (STF) is introduced and the input-to-state stability (ISS) redesign method is used in order to design a new control term able to attenuate the effects of bounded actuation disturbances and of suitably bounded observation errors. Then, it is shown that the digital implementation of STFs (continuous or not), robustified via the ISS redesign methodology, ensures the semi-global practical stability of the related quantized sampled-data closed-loop system, with arbitrarily small final target ball of the origin. Time-varying sampling intervals and the non-uniform quantization in the input/output channels are allowed. The proposed methodology is validated through a numerical example concerning a continuous stirred tank reactor (CSTR).