David Gómez-Gutiérrez

Wireless Power Transfer (WPT) has emerged as a compelling alternative to wired charging; however, efficiency and safety are highly dependent on magnetic field distribution and leakage. This work presents a compact MEMS–based magnetic field induction sensor, including its design, fabrication, and electrical characterization, for real-time diagnosis in WPT systems. The sensor employs a Ni/Cr metallic inductor fabricated on a SiO₂ substrate using standard photolithography and occupies a footprint of 5 mm × 5 mm. The device is electrically characterized through impedance, quality factor, and frequency response measurements, followed by experimental validation using an industry-standard wireless charging system and dedicated signal-conditioning circuitry. The results from inductive coupling simulations, performed with the Magpylib Python library, align with experimental data showing that the sensor accurately follows theoretical magnetic field decay, detecting AC signals between 40 mV and 140 mV with a functional limit of 30 mm. Furthermore, experimental characterization through spatial mapping successfully identifies magnetic leakage hot spots, while thermal validation via infrared thermography correlates these magnetic readings with localized temperature increases. This integrated approach supports EMC optimization and thermal risk mitigation, providing a low–cost and effective diagnostic tool for enhancing safety and performance in WPT applications.

The signal differentiation problem involves the development of algorithms that allow to recover a signal's derivatives from noisy measurements. This article develops a first-order differentiator with robustness to measurement noise, exactness in the absence of noise, optimal worst-case differentiation error, and Lipschitz-continuous output, where the output's Lipschitz constant is a tunable parameter. This combination of advantageous properties is not shared by any existing differentiator. A sample-based implementation by implicit discretization is obtained, which is quasi-exact, inherits the optimal worst-case error bound, and the Lipschitz constant translates to a discrete-time increment bound. Illustrative examples are provided to highlight the features of the developed differentiator.

A novel strategy aimed at cooperatively differentiating a signal among multiple interacting agents is introduced, where none of the agents needs to know which agent is the leader, i.e., the one producing the signal to be differentiated. Every agent communicates only a scalar variable to its neighbors; except for the leader, all agents execute the same algorithm. The proposed strategy can effectively obtain derivatives up to arbitrary order in a finite‐time under the assumption that the derivative is bounded. The strategy borrows some of its structure from the celebrated homogeneous robust exact differentiator by A. Levant, inheriting its exact differentiation capability and robustness to measurement noise. Hence, the proposed strategy can be said to perform robust exact distributed differentiation. In addition, and for the first‐time in the distributed leader‐observer literature, sampled‐data communication and bounded measurement noise are considered, and corresponding steady‐state worst‐case accuracy bounds are derived. The effectiveness of the proposed strategy is verified numerically for second‐ and fourth‐order systems, i.e., for estimating derivatives of up to first‐ and third‐order, respectively.

In this paper, we propose a new strategy to solve the multi-robot formation problem. Considering a set of holonomic robots, a decentralized algorithm is proposed to guide the robots to achieve a predefined formation while avoiding collisions with non-convex obstacles, dynamic obstacles, and other robots. Local collision avoidance is achieved using a variant of the well-known ORCA (optical reciprocal collision avoidance) algorithm. We modify this algorithm to ensure the continuity of the robots’ controls (velocities). The implementation of an online replanning algorithm, RRT, is essential to guide the robots and prevent them from getting stuck in minima. The resulting method guarantees formation convergence, and several simulations are presented to illustrate its effectiveness.

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle’s ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL−JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety.

The online differentiation of a signal contaminated with bounded noise is addressed. A differentiator is developed that generates a Lipschitz continuous output, is exact in the absence of noise, and provides the optimal worst-case accuracy among all possible exact differentiators when noise is present. This combination of features is not shared by any previously existing differentiator. Tuning of the developed differentiator is very simple, requiring only the knowledge of a bound for the second-order derivative of the signal. The approach consists in regularizing the possibly highly noisy output of a recently introduced linear adaptive robust exact differentiator and feeding it to a first-order sliding-mode filter designed to maintain optimal accuracy. The proposed regularization and filtering of this output allows trading the speed with which exactness is obtained for the feature of a Lipschitz continuous, hence less noisy, output. An illustrative example is provided to highlight the features of the developed differentiator.

There is an increasing interest in designing differentiators, which converge exactly before a prespecified time regardless of the initial conditions, that is, which are fixed‐time convergent with a predefined upper bound of their settling time (UBST), due to their ability to solve estimation and control problems with time constraints. However, for the class of signals with a known bound of their th time derivative, the existing design methodologies yield a very conservative UBST, or result in gains that tend to infinity at the convergence time. Here, we introduce a new methodology based on time‐varying gains (TVG) to design arbitrary‐order exact differentiators with a predefined UBST. This UBST is a priori set as one parameter of the algorithm. Our approach guarantees that the UBST can be set arbitrarily tight, and we also provide sufficient conditions to obtain exact convergence while maintaining bounded TVG. Additionally, we provide necessary and sufficient conditions such that our approach yields error dynamics with a uniformly Lyapunov stable equilibrium. Our results show how TVG offer a general and flexible methodology to design algorithms with a predefined UBST.

This letter addresses the problem of distributed formation tracking for a group of follower holonomic mobile robots around a reference signal. The reference signal is comprised of the geometric center of the positions of multiple leaders. This letter’s main contribution is a novel Modulated Distributed Virtual Observer (MDVO) for the reference signal. Moreover, the proposed MDVO is based on an exact dynamic consensus algorithm with a prescribed convergence time. In addition, we provide simulation examples showcasing two different application scenarios for the proposal.