Nussbaum-Type Neural Network-Based Control of Neuromuscular Electrical Stimulation With Input Saturation and Muscle Fatigue
Author ORCID Identifier
Neuromuscular electrical stimulation (NMES) is a promising technique to actuate the human musculoskeletal system in the presence of neurological impairments. The closed-loop control of NMES systems is nontrivial due to their inherent uncertain nonlinearity. In this paper, we propose a Nussbaum-type neural network (NN)-based controller for the lower leg limb NMES systems. The control accounts for model uncertainties and external disturbances in the system and, for the first time, provides a solution with rigorous stability analysis to the adaptive NMES tracking problem with input saturation and muscle fatigue. The proposed controller guarantees a uniformly ultimately bounded (UUB) tracking for the knee-joint angular position. To evaluate the control performance, a simulation study is taken, where the performance comparison with a NN controller inspired by Ge et al. (2004, "Adaptive Neural Control of Nonlinear Time-Delay Systems With Unknown Virtual Control Coefficients," IEEE Trans. Syst., Man, Cybern.-Part B, 34(1), pp. 499-516) is given. The simulation results show a good tracking performance of the proposed controller regardless of the time-varying muscle fatigue and moderate input saturation. The adaptation mechanism of the Nussbaum-type gain and the controller's robustness to the muscle fatigue and input saturation are discussed in details along with the simulations.
Publication Source (Journal or Book title)
JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS
Rui, C., Li, J., Chen, Y., Zhang, Q., Yang, R., & de Queiroz, M. (2022). Nussbaum-Type Neural Network-Based Control of Neuromuscular Electrical Stimulation With Input Saturation and Muscle Fatigue. JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS, 17 (3) https://doi.org/10.1115/1.4053325