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    Data driven identification and current control on dual active bridge systems for low power applications
    (2026-07-01)
    This work presents a data-driven methodology for the identification and control of Dual Active Bridge (DAB) DC–DC converters, aimed at reducing the complexity associated with highly nonlinear analytical models. By exploiting an angle phase-shift representation instead of a detailed PWM-based model, the proposed approach simplifies both system excitation and identification while preserving the essential power-transfer dynamics of the converter. A frequency-domain control strategy is subsequently developed based on the identified model, enabling systematic loop-shaping and robust controller design. The resulting discrete-time controller achieves accurate reference tracking and stable regulation in simulation tests, demonstrating that the proposed pipeline provides an effective and practical alternative to conventional model-based control approaches for DAB systems Key words. System identification, non lineal systems, dual active bridge, data-driven control.
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    Nonlinear trajectory tracking with a 6DOF AUV using an MRAFC controller
    (IEEE Computer Society, 2025)
    New technologies such as AUVs are used for marine exploration, considered a widespread solution in ocean monitoring, whose conventional controllers such as PID or LQR present inaccuracy in the path traversal and instability when faced with disturbances. Such that, in order to achieve sufficient precision in the path traversal and to be able to measure seabed parameters, the design of a Reference Model Adaptive Fuzzy Controller (MRAFC) is proposed. Which is a control strategy based on a combination of fuzzy systems theories using the Takagy- Sugeno model and adaptive control laws, respecting Lyapunovs nonlinear control theories to generate a robust control against inherent disturbances of the environment. Thus, the results obtained when comparing the MRAFC controller versus LQR and MRAC test controllers show better performance in different scenarios. Where the first scenario is ideal conditions, whose result is similar when the AUV is close to the origin and unstable in the LQR controller when it moves away from the design convergence point. A second scenario is considered the disturbances, obtaining unstable behaviors from the moment of the disturbance in the LQR and MRAC controllers, observing overstresses in the control variable causing chattering effect. While the last scenario is dedicated to recreate an environment with noise affecting the reading of the vehicle variables where only the MRAFC control law is able to compensate and control in a hostile environment. Therefore, based on the results of this research it is possible to identify the MRAFC controller as suitable for AUV where precision and stability are necessary.
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