Output-feedback model-reference adaptive calibration for map-based anti-jerk control of electromechanical automotive clutches

It is well known that the map-based control can reduce the computational burden of the automotive on-board controller. This paper proposes an output-feedback model-reference adaptive control algorithm to calibrate the map-based anti-jerk controller for electromechanical clutch engagement. The algorithm can be used to adaptively construct a data-driven fuzzy rule base without resorting to manual tuning, so that it can overcome the problem of conventional knowledge-based fuzzy logic design, which involves strenuous parameter-tuning work in the construction of calibration maps. To accurately define the consequent of each fuzzy rule for anti-jerk control, an output feedback law for computing the reference trajectory of clutch engagement is developed to eliminate the discontinuous slip-stick transition, whereas an adaptive controller is designed to track the reference trajectory and compensate the nonlinearity. The convergence of the proposed output-feedback model-reference adaptive control algorithm is analyzed. Simulation results indicate that the proposed method can successfully reduce the excessive vehicle jerk and frictional energy dissipation during clutch engagement as compared with the conventional knowledge-based fuzzy logic controller without fine tuning.