An Integrated Control System for Optimal Human Trunk Motion

A major desire of individuals with a spinal cord injury (SCI) is the ability to functionally move the trunk while at the same time maintaining a stable posture. The trunk movements can be actuated by Functional Neuromuscular Stimulation (FNS), which applies low-level current to the peripheral nerves to activate the paralyzed muscle, and thus generate forces and joint moments sufficient to move the limbs. Ensuring the precision and stability of the movement requires powerful control methods as the musculoskeletal dynamics of the trunk are highly complex. In this paper, an integrated control loop, which combines a joint moment (JMT) controller and a muscle (MUS) controller, is developed to control trunk motions. The JMT controller which is comprised of a prior computed feedforward torque and a model predictive controller (MPC), determines the torque vector required to achieve the motion. However, the computed torque cannot be directly applied to an actual trunk system. This limitation motivates the MUS design, where a feed-forward activation map (FAM) determines the muscle activation that can generate the desired torque vector while a neural network (NN) based correction mechanism is adjusted by an anatomy-based updating law. Therefore, when the desired motion is determined, the corresponding activation can be found and applied to the muscles of the trunk. The integrated control system is validated in simulation, in which the trunk is moved to a variety of reference postures. Simulation performance suggests that the method has a high potential for trunk control applications in individuals with SCI and other impairments.