TY - GEN
T1 - Radial-basis-functions neural network sliding mode control for underactuated manipulators
AU - Mahjoub, Sonia
AU - Mnif, Faical
AU - Derbel, Nabil
PY - 2013
Y1 - 2013
N2 - This paper presents a neural network (NN) sliding mode control (NNSMC) and indirect adaptive technique Neural Network sliding mode control (IANSMC) for underactuated robotmanipulators. The adaptive NN based on Radial Basis Functions (RBF) is used as an estimators to approximate uncertainties of the problem formulation. Adaptive learning algorithms in NNSMC are derived from the Lyapunov stability analysis. Sliding mode control and indirect adaptive technique (IANSMC) are combined to deal with modeling parameter uncertainties and bounded disturbances. The stability of the mixed controller is then proved. A radial basis function Neural Network is used to estimate system parameters and to compensate the uncertainties in the design of the sliding mode control. Neural network parameters are tuned on-line, with no off-line learning phase required. Discussions and comparisons between proposed controllers are presented. Simulation results show that the NNSM and IANSMC are betters than the traditional SMC to control underactuated manipulators.
AB - This paper presents a neural network (NN) sliding mode control (NNSMC) and indirect adaptive technique Neural Network sliding mode control (IANSMC) for underactuated robotmanipulators. The adaptive NN based on Radial Basis Functions (RBF) is used as an estimators to approximate uncertainties of the problem formulation. Adaptive learning algorithms in NNSMC are derived from the Lyapunov stability analysis. Sliding mode control and indirect adaptive technique (IANSMC) are combined to deal with modeling parameter uncertainties and bounded disturbances. The stability of the mixed controller is then proved. A radial basis function Neural Network is used to estimate system parameters and to compensate the uncertainties in the design of the sliding mode control. Neural network parameters are tuned on-line, with no off-line learning phase required. Discussions and comparisons between proposed controllers are presented. Simulation results show that the NNSM and IANSMC are betters than the traditional SMC to control underactuated manipulators.
KW - Indirect adaptive
KW - Lyapunov stability
KW - Neural Networks
KW - Sliding mode control
KW - Underactuated manipulator
UR - http://www.scopus.com/inward/record.url?scp=84883113347&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883113347&partnerID=8YFLogxK
U2 - 10.1109/SSD.2013.6564106
DO - 10.1109/SSD.2013.6564106
M3 - Conference contribution
AN - SCOPUS:84883113347
SN - 9781467364584
T3 - 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013
BT - 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013
T2 - 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013
Y2 - 18 March 2013 through 21 March 2013
ER -