Kalman filtering: theory and practice
Kalman filtering: theory and practice
Nonlinear tire force estimation and road friction identification: simulation and experiments
Automatica (Journal of IFAC)
Current-Based Slippage Detection and Odometry Correction for Mobile Robots and Planetary Rovers
IEEE Transactions on Robotics
Non-linear observer for slip parameter estimation of unmanned wheeled robots
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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Sliding mode observer is a variable structure system where the dynamics of a nonlinear system is altered via application of a high-frequency switching control. This paper presents a non-linear sliding mode observer for wheel linear slip and slip angle estimation of a single wheel based on its kinematic model and velocity measurements with added noise to simulate actual on-board sensor measurements. Lyapunov stability theory is used to establish the stability conditions for the observer. It is shown that the observer will converge in a finite time, provided the observer gains satisfy constraints based on a stability analysis. To validate the observer, linear and two-dimensional (2D) test rigs are specially designed. The sliding mode observer is tested under a variety of conditions and it is shown that the sliding mode observer can estimate wheel slip and slip angle to a high accuracy. It is also shown that the sliding mode observer can accurately predict wheel slip and slip angle in the presence of noise, by testing the performance of the sliding mode observer after adding white noise to the measurements. An extended Kalman filter is also developed for comparison purposes. The sliding mode observer is better in terms of prediction accuracy.