Robust predictive control of a wet-clutch using evolutionary algorithm optimized engagement profile

Modeling of hydraulic clutch transmissions are not straightforward because of their hard nonlinearities. Therefore a model-free optimizer is used to tune the input parameters for desired operation, thereby generating optimal reference trajectory. However these machines suffer from time varying dynamics, hence a feedback predictive control is used to track this reference against identified linear sub-models to ensure robust performance. This two step strategy is called Learning Predictive Control.