In this paper, a discrete-time controller for ground vehicles is proposed, for the tracking of desired references for lateral and yaw velocities. A novel discrete-time model of the vehicle dynamics is considered, which is obtained by means of a variational integrator known as symplectic Euler. The control inputs are given by active front steering and rear torque vectoring. Simulations have been carried out considering a virtual automobile implemented in CarSim, and a comparative study has been performed considering alternative discrete-time controllers, based on a sampled continuous-time controller and on a discrete-time vehicle model obtained by means of the explicit Euler method. The controller performance has been analyzed considering various sampling periods, showing a robustness property when the sampling period increases. Moreover, the robustness of the proposed discrete-time controller performance has been tested to show its robustness versus parameter variations.

Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications.

This study addresses the control problem in Electric Vehicles (EVs), specifically focusing on designing a robust dynamic control system capable of estimating parametric uncertainties and external disturbances. The EV under consideration is equipped with an Active Front Steering (AFS) system, which introduces an incremental steering angle to the front wheels. Additionally, the vehicle significantly enhances the powertrain, employing Permanent Magnet Synchronous Motors (PMSMs) mounted on the left and right wheel axle shafts. This configuration not only provides longitudinal traction but also facilitates appropriate yaw torque control, similar to the functionality of a Rear Torque Vectoring (RTV) system. This paper demonstrates how integrating the AFS system and RTV within the framework of integral control ensures that the vehicle can accurately track a predetermined trajectory, even in the presence of environmental disturbances, parametric uncertainties, or unmodeled dynamics. However, the proposed control strategy relies on precise information from the EV, particularly lateral velocity, which is challenging to measure directly in practical applications. To address this issue, a robust nonlinear observer is proposed to reconstruct the lateral velocity. Furthermore, High-Order Sliding Mode (HOSM) estimators are employed to approximate the parametric uncertainties and external perturbations in finite-time. The controller’s performance is evaluated through simulations of a demanding double-step steer maneuver, conducted using Simulink ^® software.

The antilock braking system (ABS) is a mechatronic system that helps a driver maintain the maneuverability of a vehicle while braking by preventing wheel lock-ups. However, the design of high-performance controllers for this type of system is complicated because of its highly nonlinear dynamics. The problem becomes even more difficult to resolve when uncertainties in the parameters appear in its dynamics. In this paper, an ABS laboratory setup mimicking a quarter car model is considered. A modified high-order sliding mode (HOSM) controller using a proportional-integral-differential (PID) control as a sliding surface was designed. This controller provides a reference value of a tire slip. The proposed controller uses a tracking error to define the slip surface through the PID controller structure, and the modified HOSM controller holds the system on the previously designed slip surface. The closed-loop system stability has been proven in the sense of Lyapunov. Finally, the ABS laboratory setup allows for experimentally checking the performance of the modified HOSM controller using a PID-sliding surface, showing a considerable increase in the efficiency of the control system compared with a PID-like controller.