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Closed-loop control of skeletal muscle is complicated by the nonlinear muscle force to length relationship and the inherent unstructured and time-varying uncertainties in available models. Some pure feedback methods have been developed with some success, but the most promising and popular control methods for neuromuscular electrical stimulation (NMES) are neural network-based methods. Neural networks provide a function approximation of the muscle model, however a function reconstruction error limits the steady-state response of typical controllers (i.e., previous controllers are only uniformly ultimately bounded). Motivated by the desire to obtain improved steady-state performance, efforts in this paper focus on the use of a neural network feedforward controller that is augmented with a continuous robust feedback term to yield an asymptotic result. Specifically, a Lyapunov-based controller and stability analysis are provided to demonstrate semi-global asymptotic tracking (i.e., non-isometric contractions) of a desired time-varying trajectory. Experimental results are provided to demonstrate the performance of the developed controller where NMES is applied through external electrodes attached to the distal-medial and proximal-lateral portion of human quadriceps femoris muscle group.