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Camless actuation offers programmable flexibility in controlling engine valve events. However, a full range of engine benefits will only be available, if the actuation system can control lift profile characteristics within a particular lift event. Control of the peak value of valve lift is a first step in controlling the profile. The paper presents an adaptive feedback control of valve lift for a springless electrohydraulic valvetrain. The adaptive control maintains peak value of lift in presence of variations in engine speed, hydraulic fluid temperature and manufacturing variability of valve assemblies. The control design includes a reduced-order model of the system dynamics. Experimental results show dynamic behavior under various operating and environmental conditions and demonstrate advantages of adaptive control over the non-adaptive type.

A dynamic model for the springless electrohydraulic valvetrain has been developed. The model speeds up the valvetrain development process by simulating effects of parameter changes, thus minimizing the number of hardware variations. It includes dynamic characteristics of check valves that enable energy recovery, hydraulic snubbers that limit seating velocity of the engine valves, and leakage in the control solenoids. A good match of the experimental data has been obtained for a single valve system, and the model calibration and validation have been completed. The known parameters are used together with some unknown calibration constants which have been tuned to match the experimental data. The simulation results for a twin valve system are also presented. The model applications for system performance analysis and for the closed-loop control of the engine valve lift are described. The cyclic variability of the experimental data is also discussed.

The development of automated guidance for agricultural tractors has addressed several basic and applied issues of agricultural equipment automation. Basic analyses have included the dynamics of steering systems and posture sensors for guidance. Applied issues have evaluated the potential of several commercial sensing systems and a commercial mechanical guidance system. A research platform has been developed based on a Case 7220 Magnum1 2-wheel drive agricultural tractor. An electrohydraulic steering system was used and characterized in support of automated guidance control. Posture sensing methods were developed using GPS, geomagnetic direction sensors (GDS), inertial, and machine vision sensing systems. Sensor fusion of GPS-inertial-machine vision and GPS-GDS-machine vision provided the most flexible and accurate guidance and capable for operation under dynamically changing field conditions.

This paper presents system identification methods of using both frequency and time domain analyses to estimate system parameters for a nonlinear electrohydraulic (E/H) tractor steering system. The frequency domain method identified parameters of the system using a linear model with a nonlinear gain function. The time domain method identified those parameters using a discrete time expression. A method of multiple models was used to represent the nonlinear system for both frequency and time domain analyses. Simulation and test results showed that the modified identification method could satisfactorily identify the parameters of the nonlinear E/H tractor steering system.