This thesis presents the iterative learning control of a fully flexible valve actuation system for
non-throttled load control of an internal combustion engine. First, a description is given of a
novel camless valve actuation system with a unique hydro-mechanical internal feedback mechanism
which simplifies the external control design. All the critical parameters describing the
engine valve event, i.e. lift, timing, duration and seating velocity can be continuously varied by
controlling the triggering timings of three two-state valves. Initial testing of a prototype experimental
setup reveals that the performance of the system (transient tracking and steady-state variability)
is influenced purely by the state of the system when the internal feedback mechanism
is activated. This feature motivates the development of a cycle-to-cycle learning-based external
control for activating the internal feedback mechanism based on the desired valve profile characteristics
and the system state. To verify the proposed control methodology, it is implemented
on the experimental system to track reference trajectories for the various valve event parameters
corresponding to the non-throttled load control of an engine during the U.S. Federal Test Procedure
(FTP) urban driving cycle. Vehicle load demand analysis is used to compute the desired
engine speed and torque requirements. Detailed dynamic valve flow simulations assuming full
flexibility of the engine valve event parameters are used to calculate the required trajectory of all
these parameters to satisfy the speed and torque requirements without the use of a throttle. The
experimental results show that the proposed framework, i.e., the valve actuation system and the
external control methodology, is able to provide excellent performance even during aggressive
transient operation. Over the 19 145 valve events of the FTP cycle, 99% of cycles had lift errors
of 0.203 mm or less, and 99% of cycles had duration errors of 4.87 crank-angle degrees or less.
Furthermore, only 11.99% of cycles had seating velocities higher than the desired bound; 99%
of cycles had seating velocities 0.0429 m/s or less over the desired bound.
University of Minnesota M.S. thesis. May 2011. Major: Mechanical engineering. Advisor:Professor Zongxuan Sun. 1 computer file (PDF); vii, 61 pages, appendix A.
Heinzen, Adam James.
Iterative learning control of a fully flexible valve actuation system for non-throttled engine load control..
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