Integrated Clutch Torque Control and Touchpoint Estimation Using Deadbeat Adaptive Backstepping

Abstract

This research investigates the discrete-time adaptive backstepping control to track the reference clutch torque and estimate the clutch touchpoint displacement simultaneously for a transfer case clutch. Note that the clutch touchpoint changes due to the clutch wear, temperature variation, and other factors. As a result, it needs to be estimated in real time for accurate clutch torque control. Instead of using a separate algorithm to estimate touchpoints, this brief proposes to utilize an integrated scheme for both tracking reference torque and estimating varying touchpoints. More specifically, the deadbeat-based control law is designed based on a systematic non-Lyapunov-function-based backstepping scheme. The stability of the closed-loop system is guaranteed, and all closed-loop states are bounded. In addition, due to the deadbeat design, the tracking and estimation convergence rates are faster than or equal to any other backstepping designs. Furthermore, the closed-loop system performance is robust to external disturbances. Finally, the proposed method is validated through simulation studies using experimental data.