Exoskeletons intended for partial assistance of walking should be able to follow the gait pattern of their users, via online adaptive control strategies rather than imposing predefined kinetic or kinematic profiles. NeuroMuscular Controllers (NMCs) are adaptive strategies inspired by the neuromuscular modeling methods that seek to mimic and replicate the behavior of the human nervous system and skeletal muscles during gait. This study presents a novel design of a NMC, applied for the first time to partial assistance hip exoskeletons. Rather than the two-phase (stance/swing) division used in previous designs for the modulation of reflexes, a 5-state finite state machines is designed for gait phase synchronisation. The common virtual muscle model is also modified by assuming a stiff tendon, allowing for a more analytical computation approach for the muscle state resolution. As a first validation, the performance of the controller was tested with 9 healthy subjects walking at different speeds and slopes on a treadmill. The generated torque profiles show similarity to biological torques and optimal assistance profiles in the literature. Power output profiles of the exoskeleton indicate good synchronization with the users' intended movements, reflected in predominantly positive work by the assistance. The results also highlight the adaptability of the controller to different users and walking conditions, without the need for extensive parameter tuning.