TY - JOUR
T1 - Event driven sliding mode control of a lower limb exoskeleton based on a continuous neural network electromyographic signal classifier
AU - Llorente-Vidrio, Dusthon
AU - Pérez-San Lázaro, Rafael
AU - Ballesteros, Mariana
AU - Salgado, Iván
AU - Cruz-Ortiz, David
AU - Chairez, Isaac
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/12
Y1 - 2020/12
N2 - This study presents an event driven automatic controller to regulate the movement of a mobile lower limb active orthosis (LLAO) triggered with the information obtained from electromyographic (EMG) signals, which are captured from the user's triceps and biceps muscles. The proposed controller has an output feedback realization including a velocity estimator algorithm based on a high order sliding mode observer. The output feedback controller implements a class of decentralized super-twisting algorithm. The controller must enforce the movement of the orthosis articulations following some defined reference trajectories. This strategy realizes a time-window dependent event driven controller for the active orthosis. The controller selects among four different routines to be executed by a patient. A differential neural network classifies the different patterns of muscle movements. This classifier succeeds in defining the correct EMG class in a 95% of the tested signals. This work senses the EMG signals from the biceps and triceps, considering a possible injury in the patient to be obtained from the quadriceps. Therefore, four upper limb routines are established to generate the corresponding classes and the four different main therapies for the LLAO. A fully instrumented and self-designed orthosis is constructed to evaluate the proposed controller including three rotational joints per leg and a mobile robot to execute translation movements.
AB - This study presents an event driven automatic controller to regulate the movement of a mobile lower limb active orthosis (LLAO) triggered with the information obtained from electromyographic (EMG) signals, which are captured from the user's triceps and biceps muscles. The proposed controller has an output feedback realization including a velocity estimator algorithm based on a high order sliding mode observer. The output feedback controller implements a class of decentralized super-twisting algorithm. The controller must enforce the movement of the orthosis articulations following some defined reference trajectories. This strategy realizes a time-window dependent event driven controller for the active orthosis. The controller selects among four different routines to be executed by a patient. A differential neural network classifies the different patterns of muscle movements. This classifier succeeds in defining the correct EMG class in a 95% of the tested signals. This work senses the EMG signals from the biceps and triceps, considering a possible injury in the patient to be obtained from the quadriceps. Therefore, four upper limb routines are established to generate the corresponding classes and the four different main therapies for the LLAO. A fully instrumented and self-designed orthosis is constructed to evaluate the proposed controller including three rotational joints per leg and a mobile robot to execute translation movements.
KW - Active orthosis
KW - Deep differential neural networks
KW - Electromyographic signals
KW - Event driven control
KW - Sliding mode control
UR - http://www.scopus.com/inward/record.url?scp=85094809226&partnerID=8YFLogxK
U2 - 10.1016/j.mechatronics.2020.102451
DO - 10.1016/j.mechatronics.2020.102451
M3 - Artículo
AN - SCOPUS:85094809226
SN - 0957-4158
VL - 72
JO - Mechatronics
JF - Mechatronics
M1 - 102451
ER -