Estimating the Roll Angle for a Two-Wheeled Single-Track Vehicle Using a Kalman Filter.

Area of Science:

• Vehicle dynamics and control systems
• Robotics and sensor fusion
• Kinematics and state estimation

Background:

• Accurate roll angle estimation is crucial for stability and control of two-wheeled vehicles.
• Existing methods may struggle with sensor noise and real-time processing demands.
• Inertial Measurement Units (IMUs) offer rich kinematic data but require sophisticated processing.

Purpose of the Study:

• To develop and validate a robust estimator for determining the roll angle of single-track vehicles during cornering.
• To address challenges posed by measurement errors and sensor noise in real-time applications.
• To leverage IMU data for precise estimation of vehicle yaw rate and roll angle.

Main Methods:

• Kinematic analysis using coordinate transformations to relate measured and global accelerations.
• Derivation of a state-space expression to model system dynamics and measurement errors.
• Implementation of a two-step measurement update Kalman filter for estimating yaw rate and roll angle.
• Real-time experimental validation using a bicycle equipped with IMU and velocity sensors.

Main Results:

• The proposed Kalman filter-based estimator demonstrated high reliability and accuracy in real-time.
• Successful estimation of roll angle and yaw rate was achieved during various maneuvers (steady turn, double lane change, sine wave turn).
• The estimator effectively processed acceleration and angular velocity signals from the IMU sensor.

Conclusions:

• The developed estimator provides a reliable and accurate solution for real-time roll angle estimation in two-wheeled vehicles.
• The study confirms the effectiveness of using IMU data with a Kalman filter for vehicle dynamics monitoring.
• This approach enhances the understanding and control of vehicle behavior during cornering maneuvers.