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In this paper recent control developments for an electromechanical valve actuator will be presented. The model-based control methodology utilizes position feedback, a nonlinear observer that provides virtual sensing of the armature velocity and current, and cycle-to-cycle learning. The controller is based on a nonlinear state-space description of the actuator that is derived based on physical principles and parameter identification. A bench-top experimental setup and a rapid control prototyping system are used to quantify the actuator performance. Experiments are conducted to measure valve release timing, transition times, and contact velocities for open- and closed-loop control schemes. Simulation results are presented for a feed-forward cycle-to-cycle learning controller.

Commercial Heavy vehicles (CHVs) are an efficient and reliable link between marine, railroad, and air transportation nodes. The vehicle braking power imposes an important constraint in the allowable vehicle speed. The compression brake augments the vehicle retarding power and is currently typically used as an on-off device by experienced drivers. Hardware and software advances allow modulation of the compression brake power through variable valve timing, and thus, enable integration of the compression brake with service brakes. To analyze how much the compression brake affects vehicle speed during braking, we develop a crank angle engine model that describes the intrinsic transient interactions between individual cylinder intake and exhaust gas process, turbocharger dynamics, and vehicle dynamics during combustion and variable brake valve timing. The model is validated using experimental data.

Substantial improvements in engine fuel efficiency, torque and reduction of emissions are available with camless actuation capable of continuous control of engine valve lift, duration and timing. A phenomenological model has been developed for an unthrottled operation that is key to efficiency gain. An adaptive nonlinear controller has been designed to coordinate intake valve lift and duration by using high sampling rate intake manifold pressure and flow sensors. The driver torque demand is satisfied, while pumping losses are minimized. Simulation results for a 4 cylinder 2.0 L engine demonstrate event-to-event tracking and cylinder-to-cylinder balancing. The controller corrects for variations in effective flow areas (e.g. valve deposits), induction ram effects, and temperature.