TY - CHAP
T1 - Adaptive neural network control of the Furuta pendulum
AU - Moreno-Valenzuela, Javier
AU - Aguilar-Avelar, Carlos
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - The purpose of this chapter is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum. Adaptation laws for the input and output weights are provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position. Using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.
AB - The purpose of this chapter is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum. Adaptation laws for the input and output weights are provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position. Using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85024112298&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-58319-8_6
DO - 10.1007/978-3-319-58319-8_6
M3 - Capítulo
AN - SCOPUS:85024112298
T3 - Intelligent Systems, Control and Automation: Science and Engineering
SP - 93
EP - 118
BT - Intelligent Systems, Control and Automation
PB - Springer Netherlands
ER -