Tracking Control for FES-Cycling based on Force Direction Efficiency with Antagonistic Bi-Articular Muscles

A functional electrical stimulation (FES)-based tracking controller is developed to enable cycling based on a strategy to yield force direction efficiency by exploiting antagonistic bi-articular muscles. Given the input redundancy naturally occurring among multiple muscle groups, the force direction at the pedal is explicitly determined as a means to improve the efficiency of cycling. A model of a stationary cycle and rider is developed as a closed-chain mechanism. A strategy is then developed to switch between muscle groups for improved efficiency based on the force direction of each muscle group. Stability of the developed controller is analyzed through Lyapunov-based methods.Comment: 8 pages, 4 figures, submitted to ACC201

Upper limb electrical stimulation using input-output linearization and iterative learning control

A control scheme is developed for multi-joint upper limb reference tracking using functional electrical stimulation (FES). In accordance with the needs of stroke rehabilitation, FES is applied to a reduced set of muscles in the arm and shoulder, with support against gravity provided by a passive exoskeletal mechanism. The approach fuses input-output linearization with iterative learning control (ILC), one of the few techniques to have been applied in clinical treatment trials with patients. This powerful hybrid control structure hence extends performance and scope of clinically proven technology for widespread application in rehabilitation robotic and FES domains. In addition to simplifying tracking and convergence properties of the stimulated joints, the framework enables conditions for the stability of unstimulated joints to be derived for the first time. Experimental results confirm tracking performance of the stimulated joints, together with unstimulated joint stability

Predictor-based tracking for neuromuscular electrical stimulation

We present a new tracking controller for neuromuscular electrical stimulation (NMES), which is an emerging technology that artificially stimulates skeletal muscles to help restore functionality to human limbs. The novelty of our work is that we prove that the tracking error globally asymptotically and locally exponentially converges to zero for any positive input delay, coupled with our ability to satisfy a state constraint imposed by the physical system. Also, our controller only requires sampled measurements of the states instead of continuous measurements and allows perturbed sampling schedules, which can be important for practical purposes. Our work is based on a new method for constructing predictor maps for a large class of time-varying systems, which is of independent interest