Pneumatics is one of the few actuation principles that can be used in an MR environment, since it can produce high forces without affecting imaging quality. However, pneumatic control is challenging, due to the air high compliance and cylinders non-linearities. Furthermore, the system's properties may change for each subject. Here, we present novel control strategies that adapt to the subject's individual anatomy and needs while performing accurate periodic gait-like movements with an MRI compatible pneumatically driven robot. In subject-passive mode, an iterative learning controller (ILC) was implemented to reduce the system's periodic disturbances. To allow the subjects to intend the task by themselves, a zeroforce controller minimized the interaction forces between subject and robot. To assist patients who may be too weak, an assist-as-needed controller that adapts the assistance based on online measurement of the subject's performance was designed. The controllers were experimentally tested. The ILC successfully learned to reduce the variability and tracking errors. The zero-force controller allowed subjects to step in a transparent environment. The assist-as-needed controller adapted the assistance based on individual needs, while still challenged the subjects to perform the task. The presented controllers can provide accurate pneumatic control in MR environments to allow assessments of brain activation.