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    Area of Science:

    • Robotics and Control Systems
    • Human-Computer Interaction
    • Automotive Engineering

    Background:

    • Driving simulators are crucial for training and safety, but limited workspace restricts realistic motion generation.
    • Motion cueing algorithms (MCAs) aim to provide realistic motion within simulator constraints.
    • Model predictive control (MPC) is increasingly used in MCAs, yet its weight tuning remains challenging.

    Purpose of the Study:

    • To propose a clear, unbiased method for optimizing MPC weights in MCAs.
    • To reduce user burden in weight tuning by incorporating user satisfaction feedback.
    • To improve the realism of motion rendering in driving simulators.

    Main Methods:

    • Developed a method to optimize MPC cost function weights using a multiobjective genetic algorithm (GA).
    • Incorporated objectives like minimizing motion inputs, rates, displacements, and sensed motion errors.
    • Utilized an interactive GA (IGA) with a web interface for decision-making based on user satisfaction and predefined constraints.

    Main Results:

    • The proposed method successfully optimized MPC weights, omitting trial-and-error processes.
    • Simulation results demonstrated superior performance compared to empirical tuning methods.
    • Achieved minimized sensed motion error, enabling more realistic motion rendering within the same workspace.

    Conclusions:

    • The novel GA-based approach provides an effective and less burdensome method for tuning MPC-based MCAs.
    • The interactive decision-making process enhances user satisfaction and optimizes motion realism.
    • This method significantly improves the fidelity of driving simulation motion compared to traditional approaches.