Up to now, not much is known about how humans control bicycles, especially when subject to large perturbations. In order to learn more about the extent to which these perturbations can be handled, a new experimental setup is required, which can deliver large perturbations to cyclists and bring them to fall. This thesis describes the detailed design and evaluation of such a setup. Several requirements are formulated regarding different experiment aspects that the setup must adhere to. The proposed design consists of a treadmill on which a subject rides a bicycle. Ropes are guided from the ends of the bicycle’s handlebar toward the front and back of the setup, where they are attached to four motor units of a robotic rope-pulling system. Based on a feed-forward conversion added with PI force feedback control, a shared controller commands the motors to maintain a tracking force or perturb the cyclist by applying a net torque on the handlebar for a short time. An active safety harness is the main feature that prevents the subject from harm. Meanwhile, motion capture recordings of strategically placed passive markers and data from inertial measurement units on the bicycle are collected. With appropriate processing, the angles and angular velocities that describe the dynamics and control of the bicycle-rider system can be obtained from these measurements. The force data is also collected, by the controller. Interesting results that can be obtained with this setup include the probabilities to fall after perturbations of variable forces and the data required to evaluate the equations of motions of the bicycle-rider system during and shortly after a perturbation. This can be used to provide a baseline against which future bicycles can be compared and it is useful for validating rider models. The experiment shows consistent performance and generates high quality measurements. Neither the 12 pilot participants nor the 26 subjects who participated in follow-up experiments encountered any safety issues. The experiment could even be improved by resolving issues regarding the motor behaviour and CPU overloads. The workload of the experiment operators could be decreased if intervention of the safety harness was trained on cycling and coupled to deactivation of the treadmill. A next step would be to automate the data processing by developing a classifier that distinguishes recoveries from falls.