Functional electrical stimulation (FES) has been shown to be an effective approach to upper-limb stroke rehabilitation, where it is used to assist arm and shoulder motion. Model-based FES controllers have recently confirmed significant potential to improve accuracy of functional reaching tasks, but they typically require a reference trajectory to track. Few upper-limb FES control schemes embed a computational model of the task; however, this is critical to ensure the controller reinforces the intended movement with high accuracy. This paper derives computational motor control models of functional tasks that can be directly embedded in real-time FES control schemes, removing the need for a predefined reference trajectory. Dynamic models of the electrically stimulated arm are first derived, and constrained optimisation problems are formulated to encapsulate common activities of daily living. These are solved using iterative algorithms, and results are compared with kinematic data from 12 subjects and found to fit closely (mean fitting between 63.2% and 84.0%). The optimisation is performed iteratively using kinematic variables and hence can be transformed into an iterative learning control algorithm by replacing simulation signals with experimental data. The approach is therefore capable of controlling FES in real time to assist tasks in a manner corresponding to unimpaired natural movement. By ensuring that assistance is aligned with voluntary intention, the controller hence maximises the potential effectiveness of future stroke rehabilitation trials.