@inproceedings{50a452daadff4c6b9ab477b2ded8e76d,
title = "Model Free Nonlinear Control with Finite-Time Estimation Applied to Closed-Loop Electrical Stimulation Induced Cycling",
abstract = "Model free or data-driven control methods are suitable for real-time applications that involve nonlinear systems with uncertainties. Human-machine interaction problems include parametric and non-parametric uncertainties that are hard to model. An alternative to develop complex models to account for these uncertainties is to exploit input-output data recorded from the human and machine to improve the performance of the combined system. In this paper, a motorized functional electrical stimulation (FES) cycling system is used to illustrate a data-driven approach that leverages past input-output data to generate an estimate of the system's non-linearly parameterizable and uncertain dynamics. This estimate is computed using an estimation law motivated by a design tool from finite-time stability and used as an input into a feedback controller. The nonlinear controller that switches across the lower-limb muscle groups and an electric motor is designed to achieve a desired speed tracking objective. A Lyapunov-based stability analysis is used to prove an asymptotic result of the tracking and estimation errors.",
keywords = "Finite-Time Control, Functional Electrical Stimulation, Lyapunov Methods, Model Free Control",
author = "Chang, {Chen Hao} and Duenas, {Victor H.} and Amit Sanyal",
note = "Publisher Copyright: {\textcopyright} 2020 AACC.; 2020 American Control Conference, ACC 2020 ; Conference date: 01-07-2020 Through 03-07-2020",
year = "2020",
month = jul,
doi = "10.23919/ACC45564.2020.9147327",
language = "English (US)",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5182--5187",
booktitle = "2020 American Control Conference, ACC 2020",
}