Optimisation-Based Tuning of a Triple-Loop Vehicle Controller to Mimic Professional Driver Performance in a DiL Simulator

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Abstract
This paper presents a simulation-based methodology for automated tuning of a triple-loop controller (steering, throttle, and braking) for a Dallara single-seater race car. The approach targets on-track driving at handling limits, where strong nonlinearities and coupled dynamics dominate, treating the vehicle as a black box. Five controller gains are optimized via derivative-free pattern search, using reference trajectories from a professional driver in a Driver-in-the-Loop (DiL) simulator. Human-likeness is promoted by penalty terms on state and control trajectories while maximizing distance over a fixed horizon as a proxy for lap-time reduction. The application uses a high-fidelity multibody vehicle model with realistic tire, suspension, and actuator dynamics in the DiL environment, rather than simplified single-track representations. Contributions are: (i) effective application of derivative-free optimization to complex, high-dimensional, black-box vehicle systems; and (ii) a systematic, reproducible procedure for automatic tuning of controller parameters with a predetermined architecture to reproduce a professional driver’s performance and embed human-likeness. Optimization required approximately 2.4 h. Results show that the optimized controller improves track coverage by 63.6 m (1.1% increase) compared to manual tuning while maintaining a realistic driving style, offering a more systematic and reliable solution than manual, trial-and-error calibration.
Funding Information
  • Dallara Automobili S.p.A (MUR 617 Ministerial Decree No. 117 of 2 March 2023)
  • PRIN 2022 PNRR “Global Stability of road vehicle motion - STAVE” (CUP I53D23005670001)

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