A method and system for intelligent control of motorcycle calipers

Abstract

This invention discloses an intelligent control method and system for motorcycle calipers. The method involves real-time acquisition and preprocessing of multi-modal data, including vehicle speed, wheel speed, piston displacement, brake disc temperature, tilt angle, and lateral acceleration, using multiple sensors. Based on the data, braking demand and adhesion coefficient are calculated, and tilt angle and lateral acceleration are used to identify straight-line, curved, or emergency braking scenarios. The data is input into a pre-trained braking control machine learning model to generate a target braking force variation curve. Based on this curve, the drive motor and caliper are controlled for braking, and the model is periodically optimized based on braking feedback and historical data. A component degradation model is used to predict the braking efficiency decay trend, and the braking force is compensated and the model is updated. Closed-loop control is used to adjust the motor current in real-time to follow the target braking force. The brake disc temperature is continuously monitored, and thermal fade compensation is triggered when the temperature exceeds a threshold. This invention achieves adaptive, precise, and reliable intelligent braking for motorcycles.

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