A reference free iterative learning strategy for wet clutch control

This paper presents a new iterative learning strategy to control wet clutches. These are complex hydraulic systems that are commonly used in automatic transmissions of heavy duty vehicles, and their control aims at performing fast and smooth engagements. Learning is used to overcome the need for complex models and to maintain performance despite large variations in the system behavior. Classical iterative learning control techniques can however not be employed directly since reference trajectories corresponding to the performance requirements are unavailable. Instead, the presented iterative learning strategy translates the performance requirements directly into an objective function and constraints, hence constituting a numerical optimization problem. After each engagement, this problem is solved in order to find the control signal for the next engagement, using a piecewise linear model for the clutch. Learning is included by using the measured response data to update the models and constraints used by the optimization problem. The presented strategy is successfully validated on an experimental test bench containing wet clutches. The learning process is shown to converge towards the desired engagement quality, and a demonstration is given of the robustness with respect to changes in the operating conditions.