Model Predictive Control With Learned External Parameters for Vehicles

The patent implements dynamic parameter adaptation by training a machine learning model to predict optimal operational parameters based on real-time vehicle states and external conditions. The controller transitions from using fixed parameters to dynamically adjusted parameters that adapt to changing conditions, resolving the contradiction between adaptability and complexity.

The patent applies preliminary action by pre-training the machine learning model offline using simulation data and historical operational information. This allows the complex adaptation logic to be prepared in advance, so that during real-time operation, the controller can quickly adjust parameters without complex real-time computations, balancing adaptability with operational simplicity.

The machine learning model is trained offline using extensive simulation data and historical operational information before deployment. This preliminary training phase allows the complex learning algorithms to be executed in advance, so that during real-time path tracking, the controller only needs to infer parameters from the trained model, reducing real-time computational complexity while maintaining high reliability.

The patent uses simulation environments to create virtual copies of the vehicle and training data before deploying the controller to the actual vehicle. The machine learning model learns from these simulated copies and synthetic training data, allowing complex parameter optimization to be performed in the simulation domain, which then transfers to improved real-world performance without proportionally increasing physical system complexity.

The system performs preliminary data collection and model training during the manufacturing and deployment phase. Historical operational data is gathered and used to train the machine learning model before the controller is deployed to the vehicle. This preliminary preparation enables precise parameter measurement and adaptation without complicating the actual deployment process, as the complex learning work is completed in advance.

The controller implements self-service by automatically adapting its operational parameters using the trained machine learning model without requiring manual calibration or configuration for each specific vehicle or condition. The system self-adjusts to different vehicle configurations, road conditions, and environmental factors, improving measurement precision while maintaining ease of deployment through automated adaptation.