Name
Title | ||
|---|---|---|
Hybrid feedforward-feedback robust adaptive extreme learning control for Euler-Lagrange systems |
Abstract | ||
|---|---|---|
In this paper, a feedforward-feedback robust adaptive extreme learning control scheme for Eular-Lagrange systems under unknown uncertainties is proposed. System unknown uncertainties can be effectively approximated through the established feedforward extreme learning neural network approxima-tor. Compared with traditional feedback approxiamation, only reference signals are needed as extreme learning approximator inputs rather than reference signals and tracking errors, which means not only the input dimensions can be reduced, but also hidden nodes can be randomly determined, and thereby eventually leading to lower computation consumption so as to facilitate the practical application. Moreover, a H-infinity term is employed to dominate the influence of approximation errors. Simulation studies are provided to demonstrate that the hybrid control scheme is effective. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICACI.2018.8377548 | 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI) |
Keywords | Field | DocType |
Eular-Lagrange system,hybrid feedforward-feedback control,extreme learning approximation,H-infinity control | Feedforward systems,Euler lagrange,Adaptive system,Control theory,Computer science,Robustness (computer science),Artificial neural network,Trajectory,Feed forward,Computation | Conference |
ISBN | Citations | PageRank |
978-1-5386-4363-1 | 0 | 0.34 |
References | Authors | |
14 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Wang | 1 | 202 | 18.93 |
| Shaofan Guo | 2 | 0 | 0.68 |
| Jian-Chuan Yin | 3 | 76 | 12.14 |
| Zhongjiu Zheng | 4 | 51 | 3.47 |
| Hong Zhao | 5 | 105 | 16.53 |