Welcome This is the research homepage for John Schmitt, Assistant Professor in the Department of Mechanical Engineering at Oregon State University. Our research group investigates the dynamics and control of a wide variety of complex mechanical, biological and thermal-fluid systems. Typically, we analyze these complex systems by creating reduced order models that substantially replicate the intrinsic system behavior observed. While the resulting models are, by design, less complex than the systems they are intended to represent, we are often able to obtain insights from the resulting analyses that would not be readily obtainable from more complex representations. While the models considered are of reduced order, the dynamics often remain nonlinear. Our group uses tools of nonlinear system theory to determine the behavior and stability of these systems as operating conditions vary. For experimental systems where models developed from first principles are inadequate, we utilize system identification techniques to produce reduced order linear models. When necessary, we utilize linear and nonlinear control techniques to stabilize the system of interest. Current areas of investigation, explained in further detail in the research section, include: Biological Systems: Our focus in this area has been modeling both planar and spatial locomotion through simple spring-mass models. Animals and insects of varying morphology utilize multiple legs as one during locomotion. The force and velocity profiles exhibited during locomotion can be shown to be well represented by simple spring-mass models such as: the Lateral Leg Spring (LLS) model in the horizontal plane, the Spring Loaded Inverted Pendulum (SLIP) model in the vertical plane, and the Spatial Spring Loaded Inverted pendulum in three dimensions. While the planar models in the horizontal and sagittal planes can exhibit self-stabilizing behavior with constant leg touch-down angles, such leg touch-down protocols are unable to stabilize gaits in the three dimensional model. Current research therefore focuses on generating simple control methodologies and leg recirculation protocols that will modify the leg touchdown angles in an appropriate manner to produce stable periodic gaits for both the planar and spatial locomotion models. Ultimately, we plan to embed these lower dimensional models into higher degree of freedom robotic representations, such that control of the higher dimensional system reduces to control of the reduced order model. Mechanical Systems: Our current research in this area focuses on the dynamics and control of a four rotor RC helicopter, the Draganflyer. In this work, we utilize system identification techniques to produce a reduced order model of the aircraft dynamics, and utilize the resulting model in a model predictive control scheme to obtain desired flight characteristics and control. Control of Microfluidic Systems: Our research projects in this area focus on improving heat transfer effectiveness through control of fluid flow. One project investigates the use of model predictive control of microvalves for fluid flow through microchannels. In this work, we attempt to reduce the negative effects of vapor bubble formation in microchannels by effectively redistributing the fluid flow via microvalves in the microchannel array. Eliminating vapor bubbles in this manner will serve to improve the heat transfer effectiveness in such configurations. A separate proejct investigates the use of system identifcation techniques combined with classical control methodologies to improve heat transfer effectiveness in a desorber.