Name
Title | ||
|---|---|---|
Adaptive self-constructing radial-basis-function neural control for MIMO uncertain nonlinear systems with unknown disturbances |
Abstract | ||
|---|---|---|
In this paper, an adaptive self-constructing RBF neural control (AS-RBFNC) scheme for trajectory tracking of MIMO uncertain nonlinear systems with unknown time-varying disturbances is proposed. System uncertainties and unknown dynamics can be exactly identified online by a self-constructing RBF neural network (SC-RBFNN) which is implemented by employing dynamically constructive hidden nodes according to the structure learning criteria including hidden node generating and pruning. The globally asymptotical stability of the entire AS-RBFNC control system is derived from Lyapunov approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889644 | IJCNN |
Keywords | Field | DocType |
mimo uncertain nonlinear systems,uncertain systems,lyapunov approach,neurocontrollers,asymptotic stability,self-constructing rbf neural network,time-varying systems,sc-rbfnn,learning systems,structure learning criteria,time-varying disturbances,as-rbfnc control system,adaptive control,nonlinear control systems,adaptive self-constructing radial-basis-function neural control,trajectory tracking,globally asymptotical stability,trajectory control,dynamically constructive hidden nodes,mimo systems,lyapunov methods,adaptive systems,trajectory,neural networks | Nonlinear system,Control theory,Radial basis function neural,Computer science,MIMO,Adaptive control | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Wang | 1 | 333 | 18.88 |
| Bijun Dai | 2 | 0 | 0.68 |
| Yancheng Liu | 3 | 80 | 4.66 |
| Min Han | 4 | 164 | 8.79 |