In this paper, a controller tuning methodology for unknown linear systems is proposed. The approach requires a set of experimental data generated by the plant for a single experiment. For a controller structure parametrized as a linear combination of basis functions, the procedure allows to find a feasible set of parameters compatible with a given performance criterion expressed as a desired complementary sensitivity function. The algorithm deals with unknown but bounded noises and does not require any statistical hypotheses on disturbances nor open loop operation to capture the experimental data. The performance of the procedure is illustrated by numerical examples.

In this chapter, we compare two approaches to the data-driven control (DDC) design problem. In this framework, the controllers are directly identified from data avoiding the plant identification step. The analyzed approaches are virtual reference feedback tuning (VRFT) and set-membership tuning (SMT) controller. They differ in the assumptions about the noise affecting the experimental data and the criteria to select an optimal controller. The former strategy assumes an stochastic description of the unknown signals, while the latter imposes an unknown but bounded (UBB) noise structure. Both methodologies are described and their main theoretical results are reported. The two approaches are evaluated on an experimental case study, consisting of the controller tuning for an active suspension (AS) system. Three Monte Carlo experiments are performed, where 100 controllers are derived from data affected by measurement noise using both methods, and their performance is evaluated on the experimental test-bench. Results show that both approaches offer a similar performance when the size of the dataset is much larger than the dimension of the controller parameters vector. However, for reduced datasets, the SMT approach gives consistent results while the VRFT method is not able to extract useful information. The same behavior is observed when the two approaches are applied to datasets affected by process disturbances. It is observed that the root mean squared error of the resulting loops can be up to 30 times lower using the set membership method for reduced datasets.

Este trabajo presenta el diseño, implementación y evaluación de una secuencia didáctica para la enseñanza de conceptos tecnológicos de microelectromovilidad, orientada al uso de un banco de pruebas de un vehículo eléctrico liviano. La propuesta se fundamenta en el modelo instruccional 5E y se desarrolla bajo el enfoque metodológico de la investigación basada en diseño (IBD), mediante dos iteraciones sucesivas en contextos reales de formación tecnológica. La secuencia didáctica integra sesiones teóricas expositivas y sesiones prácticas guiadas, con el propósito de favorecer la comprensión progresiva de los sistemas eléctricos y electrónicos que conforman un scooter. Para la evaluación de la intervención se empleó un enfoque mixto, que incluyó pre-test y pos-test, cuestionarios de percepción y el análisis de productos elaborados por los participantes. Los resultados evidencian un incremento significativo en los conocimientos tecnológicos y procedimentales asociados a la electromovilidad, así como un aumento en la percepción de confianza de los participantes respecto a su aprendizaje. Asimismo, el análisis de la primera iteración permitió identificar oportunidades de mejora, particularmente en el diagnóstico de fallas, abordadas mediante ajustes en la segunda iteración, mejorando la coherencia entre objetivos, actividades y recursos didácticos. Los hallazgos sugieren que la articulación del modelo 5E dentro de una secuencia didáctica bajo el enfoque IBD constituye una estrategia efectiva para la enseñanza de conceptos tecnológicos de microelectromovilidad en la formación de estudiantes de licenciatura en tecnología.

This article presents an experimental–computational methodology for evaluating the state of charge (SoC), energy consumption, terminal-voltage behavior, and driving range of a 350 W electric scooter powered by a 36 V, 7.8 Ah lithium-ion battery. The test was carried out using a 117 s elementary urban driving cycle derived from the low-speed section of the New European Driving Cycle (NEDC) and limited to the 32 km/h operating speed of the scooter. Laboratory measurements were performed on rollers under controlled conditions. Battery current and terminal voltage were recorded during the discharge test. The experimental SoC was reconstructed from the measured current by trapezoidal Coulomb counting. The voltage-derived SoC values included in the original laboratory file were kept only for traceability, since they did not correspond to current integration. A MATLAB/Simulink model was developed to reproduce the driving cycle, longitudinal vehicle dynamics, DC motor demand, battery current, and SoC evolution. The valid experimental endpoint occurred at 5233 s, when the terminal voltage reached 31.50 V. At this point, the tested distance was 16.49 km, the discharged capacity was 5.817 Ah, and the final experimental SoC was 25.42%. The simulation produced a discharged capacity of 5.147 Ah and a final SoC of 34.01%, with a charge deviation of 11.51%. Energy consumption was also evaluated from the measured and simulated electrical power. The experimentally integrated discharged energy was 208.10 Wh, equivalent to 12.62 Wh/km. The simulated electrical demand was 184.41 Wh, equivalent to 11.18 Wh/km. A semiempirical terminal-voltage reconstruction, based on the simulated SoC, current demand, an open-circuit-voltage curve, and a fixed internal resistance, reproduced the global voltage-decay trend observed in the experiment. The simplified model captured the general discharge behavior, although it underestimated the measured charge and energy demand. The proposed workflow provides a reproducible basis for comparing manufacturer-declared range, laboratory measurements, current-based SoC reconstruction, energy consumption, and simplified simulation results in light electric vehicles.