Neural-network-based nonlinear model predictive tracking control of a pneumatic muscle actuator-driven exoskeleton

Pneumatic muscle actuators &#x0028 PMAs &#x0029 are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries, such as strokes, spinal cord injuries, etc., to accomplish rehabilitation tasks. However, because PMAs have nonl...

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Vydáno v:IEEE/CAA journal of automatica sinica Ročník 7; číslo 6; s. 1478 - 1488
Hlavní autoři: Cao, Yu, Huang, Jian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway Chinese Association of Automation (CAA) 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Témata:
Actuation
Actuators
Algorithms
Approximation
Control stability
Control systems design
Controllers
Exoskeletons
Feedback control
Gait
Gaussian process
Gaussian processes
Legged locomotion
Mathematical analysis
Muscles
Neural networks
Nonlinear control
Optimization
Orthoses
Predictive control
Predictive models
Rehabilitation
Spinal cord injuries
Subsystems
System dynamics
Task complexity
Tracking control
Training
model predictive control
Echo state Gaussian process
neural network
pneumatic muscle actuators-driven exoskeleton
ISSN:2329-9266, 2329-9274
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Popis
Shrnutí:Pneumatic muscle actuators &#x0028 PMAs &#x0029 are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries, such as strokes, spinal cord injuries, etc., to accomplish rehabilitation tasks. However, because PMAs have nonlinearities, hysteresis, and uncertainties, etc., complex mechanisms are rarely involved in the study of PMA-driven robotic systems. In this paper, we use nonlinear model predictive control &#x0028 NMPC &#x0029 and an extension of the echo state network called an echo state Gaussian process &#x0028 ESGP &#x0029 to design a tracking controller for a PMA-driven lower limb exoskeleton. The dynamics of the system include the PMA actuation and mechanism of the leg orthoses; thus, the system is represented by two nonlinear uncertain subsystems. To facilitate the design of the controller, joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP. A gradient descent algorithm is employed to solve the optimization problem and generate the control signal. The stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system dynamics. Simulations and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.
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ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2020.1003351