Name
Abstract | ||
|---|---|---|
Controller parameter tuning is an integral part of control engineering practice. Existing tuning methods usually start with an accurate mathematical model of the controlled system, which may pose some challenges for practicing engineers dealing with real systems. As such, parameter optimization and adaptation are treated as two independent steps during tuning. To address these issues, we propose a new, online parameterized controller tuning method for a general nonlinear dynamic system. This tuning method is based on direct heuristic dynamic programming (direct HDP), a model-free algorithm in the approximated dynamic programming (ADP) family. By using a Lyapunov stability approach, we provide uniformly ultimately bounded (UUB) results under some mild conditions for controller parameters, the critic neural network weights, and the action neural network weights. Simulation studies based on the benchmark cart-pole system demonstrate adaptability and optimization capabilities of the proposed controller parameter tuning method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889869 | IJCNN |
Keywords | Field | DocType |
approximate dynamic programming,lyapunov stability approach,direct hdp,uniformly ultimately bounded results,neurocontrollers,adp family,control engineering,learning (artificial intelligence),nonlinear dynamical systems,real systems,nonlinear dynamic system,heuristic programming,controlled system mathematical model,online controller parameter adaptation,uub results,critic neural network weights,parameter optimization,action neural network weights,direct heuristic dynamic programming,controller parameter tuning methods,dynamic programming,control engineering practice,stability,model-free algorithm,online parameterized controller tuning method,lyapunov methods,cart-pole system,tuning,convergence,learning artificial intelligence,artificial neural networks,function approximation | Adaptability,Dynamic programming,Control theory,Parameterized complexity,Nonlinear system,Computer science,Control theory,Lyapunov stability,Artificial intelligence,Artificial neural network,Machine learning,Bounded function | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wentao Guo | 1 | 54 | 4.60 |
| Feng Liu | 2 | 269 | 28.35 |
| Jennie Si | 3 | 746 | 70.23 |
| Shengwei Mei | 4 | 196 | 34.27 |