Slope and Mass Estimation for Semi-Trailer Truck Considering Pitch Angle Variations

Accurately estimating the road slope and mass of a tractor-trailer is crucial from a control perspective as it significantly impacts the dynamic behavior. However, achieving accurate estimation of these variables poses a challenge, especially in scenarios involving mass variations due to loading on the semi-trailer. This difficulty arises from the need to appropriately decouple among the longitudinal acceleration, the acceleration due to road slope and acceleration sensor bias due to pitch angle. To solve this difficulty, this study proposes a road slope and mass estimation framework that accounts for changes in pitch angle. Firstly, a kinematic model is introduced including the longitudinal acceleration and the pitch angle. Secondly, a physically feasible neural network-based pitch angle estimator is designed to determine the pitching effect in the longitudinal acceleration sensor data. The input features are derived based on the pitch dynamics, and pitch bias is additionally introduced to consider the mass variation during weight loading/unloading. Then, a Kalman Filter (KF) is designed to estimate the longitudinal acceleration and slope angle with considering the estimated pitch angle. Finally, a Recursive Least Squares (RLS)-based mass estimation method is presented utilizing the estimated longitudinal acceleration and the estimated road slope. Outlier data are filtered using the Random Sample Consensus (RANSAC) algorithm to improve the performance of the RLS. The proposed estimation method is validated through simulations.