Mattia Bruschetta

Marco Cortese, Antonio Gallina, Matteo Grandin et al.

Model Predictive Control (MPC) has been widely applied to autonomous vehicle drifting. Assisted drifting, that is where the driver remains in the loop, is still comparatively underexplored. Existing approaches often rely on restrictive assumptions, such as precomputed drift equilibria, full actuation authority, or prior path knowledge, which limit applicability to expert drivers. This paper proposes a nonlinear model predictive control (NMPC) framework for assisted drifting on a rear-wheel-drive vehicle. Through steer-by-wire and drive-by-wire interfaces, the controller decouples driver commands from direct actuator inputs, allowing the driver to regulate the desired sideslip through the steering wheel while the NMPC maintains vehicle stability. A dedicated activation logic ensures that the controller engages only under deliberate driver intent. High-fidelity simulations show that the proposed architecture can stabilize drifting maneuvers using a simple single-track prediction model with basic tire dynamics, even when the sideslip reference is continuously varied by the driver.

Mattia Scarpa, Francesco Pase, Ruggero Carli et al.

Digital twins for power electronics require accurate power losses whose direct measurements are often impractical or impossible in real-world applications. This paper presents a novel hybrid framework that combines physics-based thermal modeling with data-driven techniques to identify and correct power losses accurately using only temperature measurements. Our approach leverages a cascaded architecture where a neural network learns to correct the outputs of a nominal power loss model by backpropagating through a reduced-order thermal model. We explore two neural architectures, a bootstrapped feedforward network, and a recurrent neural network, demonstrating that the bootstrapped feedforward approach achieves superior performance while maintaining computational efficiency for real-time applications. Between the interconnection, we included normalization strategies and physics-guided training loss functions to preserve stability and ensure physical consistency. Experimental results show that our hybrid model reduces both temperature estimation errors (from 7.2+-6.8°C to 0.3+-0.3°C) and power loss prediction errors (from 5.4+-6.6W to 0.2+-0.3W) compared to traditional physics-based approaches, even in the presence of thermal model uncertainties. This methodology allows us to accurately estimate power losses without direct measurements, making it particularly helpful for real-time industrial applications where sensor placement is hindered by cost and physical limitations.