Name
Title | ||
|---|---|---|
A new PID-type Fuzzy neural network controller based on Genetic Algorithm with improved Smith predictor |
Abstract | ||
|---|---|---|
Owing to the problem of control difficulty for the complex system, which has the characteristics of the large inertia, the pure time-delay and the model uncertainty in the industrial processes, a new PID-type fuzzy neural network controller (FNNC) based on Takagi-Sugeno-Kang (TSK) inference is proposed. Real-coded Chaotic Quantum-inspired Genetic Algorithm (RCQGA) is used to optimize the membership function parameters and TSK parameter sets simultaneously with faster convergence speed and more powerful optimizing ability. The pure time-delay effect of the complex object is compensated by a Smith predictor combined with Radial Basis Function (RBF) neural network identifier. The structure and control tactics of the controller are presented and tested by simulations and experiments in the heating furnace system. The proposed algorithm, as confirmed by the results of simulation and experiment compared with the Smith-Fuzzy-PID controller, exhibits good dynamic adjustment, high steady-state accuracy, strong resistant ability to interference and good robustness. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CDC.2009.5399836 | CDC |
Keywords | Field | DocType |
robustness,time delay,smith-fuzzy-pid controller,takagi-sugeno-kang inference,radial basis function networks,model uncertainty,neurocontrollers,real-coded chaotic quantum-inspired genetic algorithm,complex system,industrial processes,pid-type fuzzy neural network controller,membership function parameters,genetic algorithms,smith predictor,radial basis function neural network identifier,heating furnace system,fuzzy neural nets,three-term control,steady state,fuzzy neural network,artificial neural networks,membership function,fuzzy control,pid controller,radial basis function,interference,genetic algorithm,heating,power optimization | Control theory,Smith predictor,PID controller,Control theory,Computer science,Robustness (computer science),Fuzzy control system,Artificial neural network,Membership function,Genetic algorithm | Conference |
ISSN | ISBN | Citations |
0191-2216 E-ISBN : 978-1-4244-3872-3 | 978-1-4244-3872-3 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruiqi Wang | 1 | 64 | 15.44 |
| Ke Li | 2 | 0 | 1.01 |
| Naxin Cui | 3 | 1 | 1.72 |
| Chenghui Zhang | 4 | 268 | 38.20 |