Name
Abstract | ||
|---|---|---|
For a nonlinear discrete-time Multi-Input Multi-Output (MIMO) system, a Hierarchical Multiple Models Neural Network Decoupling Controller (HMMNNDC) is designed in this paper. Firstly, the nonlinear system's working area is partitioned into several sub-regions by use of a Self-Organizing Map (SOM) Neural Network (NN). In each sub-region, around every equilibrium point, the nonlinear system can be expanded into a linear term and a nonlinear term. The linear term is identified by a BP NN trained offline while the nonlinear term by a BP NN trained online. So these two BP NNs compose one system model. At each instant, the best sub-region is selected out by the use of the SOM NN and the corresponding multiple models set is derived. According to the switching index, the best model in the above model set is chosen as the system model. Then the nonlinear term of the system are viewed as measurable disturbance and eliminated by the choice of the weighting polynomial matrices. The simulation example shows that the better system response can be got comparing with the conventional NN decoupling control method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87734-9_14 | ISNN (2) |
Keywords | Field | DocType |
bp nn trained online,better system response,system model,nonlinear system,conventional nn decoupling control,nonlinear term,linear term,som nn,bp nn,nonlinear hierarchical multiple models,neural network decoupling controller,nonlinear discrete-time,system modeling,discrete time,neural network,equilibrium point,indexation,decoupling,hierarchical | Control theory,Weighting,Nonlinear system,Polynomial,Control theory,Computer science,Decoupling (cosmology),Equilibrium point,Artificial intelligence,Artificial neural network,System model,Machine learning | Conference |
Volume | ISSN | Citations |
5264 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Wang | 1 | 5 | 3.76 |
| Hui Yang | 2 | 18 | 8.01 |
| Shaoyuan Li | 3 | 463 | 65.77 |
| Wenxin Liu | 4 | 96 | 8.39 |
| Li Liu | 5 | 46 | 8.81 |
| David A. Cartes | 6 | 64 | 11.09 |