The resonant wave modes in monomodal and multimodal planar Surface Plasmon Resonance (SPR) sensors and their response to a bidimensional array of gold nanoparticles (AuNPs) are analyzed both theoretically and experimentally, to investigate the parameters that rule the correct nanoparticle counting in the emerging metal nanoparticle-amplified surface plasmon resonance (PA-SPR) spectroscopy. With numerical simulations based on the Finite Element Method (FEM), we evaluate the error performed in the determination of the surface density of nanoparticles σ when the Maxwell-Garnett effective medium theory is used for fast data processing of the SPR reflectivity curves upon nanoparticle detection. The deviation increases directly with the manifestations of non-negligible scattering cross-section of the single nanoparticle, dipole-dipole interactions between adjacent AuNPs and dipolar interactions with the metal substrate. Near field simulations show clearly the set-up of dipolar interactions when the dielectric thickness is smaller than 10 nm and confirm that the anomalous dispersion usually observed experimentally is due to the failure of the effective medium theories. Using citrate stabilized AuNPs with a nominal diameter of about 15 nm, we demonstrate experimentally that Dielectric Loaded Waveguides (DLWGs) can be used as accurate nanocounters in the range of surface density between 20 and 200 NP/µm², opening the way to the use of PA-SPR spectroscopy on systems mimicking the physiological cell membranes on SiO₂ supports.

Model predictive control (MPC) is a powerful and widely used technique to address the control challenges in power converters as the grid interface for renewable energy systems. This technique combines closed-loop control with error and control effort minimization; however, its design is challenging, and we know little about how the controller parameters affect the closed-loop performance of grid-connected voltage source converters. In this study, we applied a MPC direct power control with modulation for a grid-connected power converter with inductive filter. For the controller design, we proposed an initial set based on the power converter’s nominal setup. Then, we define the range of settings to guarantee stability by analyzing the closed-loop poles of the system. The fine-tuning to improve the performance can be identified visually using the performance maps built from simulations of the control system, simultaneously sweeping the time horizons of the predictive model and the weight factors of the cost function. Experimental results on a low-power bench demonstrate the excellent performance of the designed controller, following and even outperforming the classical proportional-integral (PI) controller and other advanced control techniques.

The intensification of policies to combat climate change has led to the electrification of the transport sector for off-road applications such as agricultural machinery. Among the electric drives eligible for the electric traction of these vehicles, induction motors stand out due to their low cost, construction simplicity, and efficient control methods. This paper proposes a predictive current control of induction motors for agricultural electric traction based on the Lyapunov stability theory. The cost function is designed to minimize the error between the prediction and the actual voltage vector applied by the converter capable of exponentially stabilizing the system. The closed-loop stability of the system is ensured by designing the control inputs based on a Lyapunov function for discrete-time systems. Experimental tests carried out in the laboratory evaluate the control method’s performance in agricultural electric traction applications. The load torque is modeled following the longitudinal model, considering typical field operating conditions. The results show steady-state performance for the current components within an acceptable operating margin while keeping the mechanical speed within the defined error band. The transient responses are short and confirm the feasibility of the control when it operates over an agricultural tractor duty cycle, validating the proposed control strategy.

Model predictive control (MPC) is a well-established approach for applications using grid-connected inverters. However, because the MPC is a model-based method, its performance may degrade due to parametric errors and large perturbations in grid voltage or load current. Predictive repetitive control (PRC) combines the conventional MPC with repetitive control and presents an improved potential for dealing with parametric uncertainties in this application context that is not fully explored in the technical literature. This paper proposes a robust PRC current control in the synchronous rotation frame for a grid-connected inverter under parametric uncertainty. The concept of robust stability (RS) margin, derived from the Singular Value Decomposition (SVD), was used to examine how the PRC controller tuning affects the robustness under parametric uncertainty and grid voltage disturbance. Experimental results on a 1 kW workbench validated the robustness of the proposed PRC controller, maintaining satisfactory performance under nominal and severe parametric uncertainty, satisfying the IEEE Std. 1547.2-2008. In addition, it was demonstrated that the proposed PRC provides greater robustness to parametric uncertainty than the conventional MPC and the classical PI controllers.

Electric vehicle (EV) adoption has increased significantly as a result of growing efforts to combat climate change. The vehicle-to-grid (V2G) mode of operation can significantly reduce the intermittency of power demand from EV chargers by using the fleet as a distributed energy resource. However, operating under distorted grid voltage, the stability of the EV charger control system can be compromised. To address this issue, this work proposes a predictive direct power control based on the Lyapunov stability theory for an EV charger grid-side converter. The controller employs Lyapunov stability concepts for discrete-time systems to converge controlled variables to reference values, rejecting grid voltage disturbance. Experimental tests were conducted on an EV charger workbench to evaluate the performance of the control method. The power reference was calculated to enhance power quality by injecting a smooth sinusoidal current into the grid, compensating for voltage distortion. The results show that the active and reactive power are performing efficiently within an acceptable operating margin while maintaining the total harmonic distortion within the 5% band. The feasibility of the control method was validated during the discharge cycle of the EV battery. In addition, the proposed controller outperformed the classical PI controller.

An alternative approach to classical surface plasmon resonance spectroscopy is dielectric-loaded waveguide (DLWG) spectroscopy, widely used in the past decades to investigate bio-interaction kinetics. Despite their wide application, a successful and clear approach to use the DLWGs for the one-step simultaneous determination of both the thickness and refractive index of organic thin films is absent in the literature. We propose here, for the first time, an experimental protocol based on the multimodal nature of DLWGs to be followed in order to evaluate the optical constants and thickness of transparent thin films with a unique measurement. The proposed method is general and can be applied to every class of transparent organic materials, with a resolution and accuracy which depend on the nature of the external medium (gaseous or liquid), the geometrical characteristics of the DLWG, and the values of both the thickness and dielectric constant of the thin film. From the experimental point of view, the method is demonstrated in a nitrogen environment with an accuracy of about 3%, for the special case of electroluminescent thin films of Eu³⁺β-diketonate complexes, with an average thickness of about 20 nm. The high value of the refractive index measured for the thin film with the Eu(btfa)₃(t-bpete) complex was confirmed by the use of a spectroscopic model based on the Judd–Ofelt theory, in which the magnetic dipole transition ⁵D₀ → ⁷F₁ (Eu³⁺) for similar films containing Eu³⁺ complexes is taken as a reference. The DLWGs are finally applied to control the refractive index changes of the organic thin films under UVA irradiation, with potential applications in dosimetry and monitoring light-induced transformation in organic thin films.