Studies have shown that feedback linearization can provide an effective controller for many types of nonlinear systems. It is known, however, that these controllers are not robust, in particular to model uncertainties as the feedback linearization process involves canceling of nonlinearities in the dynamics using an exact model which is seldom available. Although there are several strategies to add robustness, recent work on sensitivity theory has shown that it can provide the least conservative design for robust feedback linearization. This is achieved by adjusting the control input to minimize the sensitivity. The work in this thesis develops the robust feedback linearization (RFL) methodology further by extending it to a new class of non-linear systems. This research presents a methodology for designing a RFL controller in conjunction with previous work on integrated robust optimal design (IROD) for hydraulically controlled multibody systems.With growing world populations the total output of the agricultural industry will need to increase with it. It has been shown that a significant portion of yield losses occur during harvest, and specifically at the header of the combine harvester. One way to improve this is by improved header height tracking. Promising research has shown that integrated mechanical plant and controller design can provide a better optimal controller than previously possible, but those techniques focus on the mechanical system only and do not account for hydraulic actuator dynamics. However, in practice, hydraulic systems pose control challenges because they are highly nonlinear and the system parameters can vary significantly. The proposed RFL methodology offers an ideal solution to this problem and the work in this thesis is dedicated to developing this methodology. Details are given about the mechanical and hydraulic plants as well as the development of a nominal feedback linearization controller. Then the controller is rendered robust to uncertainties in the bulk modulus by deriving the sensitivity dynamics and control adjustment. Finally, the controller performance is tested over a variety of simulated conditions and is compared to the current industry standard, the PID controller. The results show that the RFL controller greatly improves header height tracking with reduced input power and is robust to bulk modulus uncertainties.
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