TY - JOUR
T1 - Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function
AU - Ren, Beibei
AU - Ge, Shuzhi Sam
AU - Tee, Keng Peng
AU - Lee, Tong Heng
PY - 2010/8
Y1 - 2010/8
N2 - In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.
AB - In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.
KW - Barrier function
KW - neural networks (NNs)
KW - output feedback nonlinear systems
KW - unknown functions
UR - http://www.scopus.com/inward/record.url?scp=77955516678&partnerID=8YFLogxK
U2 - 10.1109/TNN.2010.2047115
DO - 10.1109/TNN.2010.2047115
M3 - Article
C2 - 20601313
AN - SCOPUS:77955516678
SN - 1045-9227
VL - 21
SP - 1339
EP - 1345
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
IS - 8
M1 - 5499019
ER -