The study of human locomotion has gained more attention recently with the availability of better analytic and computational tools with which to examine it. A subject under much study within the field today is the effort to model human motor control systems using control systems methods. Analytic, computational, and experimental studies of locomotion can produce models that provide further insight into the design and function of human systems, as well as provide directions for research into therapies for muscle and nerve related disorders affecting these systems. This thesis examines how computational methods can be utilized to study the functionality of these systems. Building on past research, dynamic models for a human skeletal system pedaling a bicycle are used as a basis for examining various methods of implementing inputs that will control the cycling. Two models are used – a three degree-of-freedom model implementing ideal torque inputs at the hip, knees, and feet, and a one degree-of-freedom model involving inputs at the hip and knee only. Both models are characterized by highly nonlinear dynamics, requiring the use of nonlinear analysis, optimization theory, and computational methods for examination. Control of the one degree-of­-freedom model has been addressed in previous work; here, parameterization of the control and the process of learning it is examined. Next, control strategies for the more complex three degree-of-freedom model are developed. Finally, results for upright and recumbent cycling are compared using the three degree-of-freedom model.