Praying mantises hunt by standing on their meso- and metathoracic legs and using them to rotate and translate (together, 'pivot') their bodies toward prey. We have developed a neuromechanical software model of the praying mantis Tenodera sinensis to use as a platform for testing postural controllers that the animal may use while hunting. Previous results showed that a feedforward model was insufficient for capturing the diversity of posture observed in the animal (Szczecinski et al 2014 Biomimetic and Biohybrid Syst. 3 296-307). Therefore we have expanded upon this model to make a flexible controller with feedback that more closely mimics the animal. The controller actuates 24 joints in the legs of a dynamical model to orient the head and translate the thorax toward prey. It is controlled by a simulation of nonspiking neurons assembled as a highly simplified version of networks that may exist in the mantid central complex and thoracic ganglia. Because of the distributed nature of these networks, we hypothesize that descending commands that orient the mantis toward prey may be simple direction-of-intent signals, which are turned into motor commands by the structure of low-level networks in the thoracic ganglia. We verify this through a series of experiments with the model. It captures the speed and range of mantid pivots as reported in other work (Yamawaki et al 2011 J. Insect Physiol. 57 1010-6). It is capable of pivoting toward prey from a variety of initial postures, as seen in the animal. Finally, we compare the model's joint kinematics during pivots to preliminary 3D kinematics collected from Tenodera.