TY - JOUR
T1 - Trajectory tracking control of a self-balancing robot via adaptive neural networks
AU - Gandarilla, Isaac
AU - Montoya-Cháirez, Jorge
AU - Santibáñez, Víctor
AU - Aguilar-Avelar, Carlos
AU - Moreno-Valenzuela, Javier
N1 - Publisher Copyright:
© 2022
PY - 2022/11
Y1 - 2022/11
N2 - In order to ensure trajectory tracking on a two degrees-of-freedom self-balancing robot (SBR) a control scheme, based on the combination of adaptive neural networks and input–output linearization, is presented in this paper. Both external and internal dynamics are analyzed and proof of uniform ultimate boundedness of the position errors is given. The controller performance is assessed via real-time experiments and compared with respect to other control schemes. Better tracking accuracy and disturbance rejection capability is produced by the introduced controller.
AB - In order to ensure trajectory tracking on a two degrees-of-freedom self-balancing robot (SBR) a control scheme, based on the combination of adaptive neural networks and input–output linearization, is presented in this paper. Both external and internal dynamics are analyzed and proof of uniform ultimate boundedness of the position errors is given. The controller performance is assessed via real-time experiments and compared with respect to other control schemes. Better tracking accuracy and disturbance rejection capability is produced by the introduced controller.
KW - Adaptive neural network
KW - Input–output linearization
KW - Real-time experiments
KW - Self-balancing robot
KW - Trajectory tracking control
UR - http://www.scopus.com/inward/record.url?scp=85139028557&partnerID=8YFLogxK
U2 - 10.1016/j.jestch.2022.101259
DO - 10.1016/j.jestch.2022.101259
M3 - Artículo
AN - SCOPUS:85139028557
SN - 2215-0986
VL - 35
JO - Engineering Science and Technology, an International Journal
JF - Engineering Science and Technology, an International Journal
M1 - 101259
ER -