Neuromuscular disorders (NMDs) may induce difficulties to perform daily life activities in autonomy. For people with NMDs affecting the upper-limb mobility, dynamic arm supports (DASs) turn out to be relevant assistive devices. In particular, active DASs benefit from an external power source to support severely impaired people. However, commercially available active devices are controlled with push buttons, which adds cognitive load and discomfort. To alleviate this issue, we propose a new force-based assistive control framework. In this preliminary work, we focus on the computation of a feedforward force to compensate upper-limb gravity. Four strategies based on a biomechanical model of the upper limb, tuned using anthropometric measurements, are proposed and evaluated. The first one is based on the potential energy of the upper-limb, the second one makes a compromise between the shoulder and elbow torques, the third one minimizes the sum of the squared user joint torques and the last one uses a probabilistic approach to minimize the expected torque norm in the presence of model uncertainties. These strategies have been evaluated quantitatively through an experiment including nine participants with an active DAS prototype. The activity of six muscles was measured and used to compute the mean effort index (MEI) which represents the global effort required to maintain the pose. A statistical analysis shows that the four strategies significantly lower the MEI (p-value < 0.001).