Name
Abstract | ||
|---|---|---|
This paper discusses two approaches for neural control of a flexible link using the Feedback-Error-Learning technique. This technique aims to acquire the inverse dynamics model of the plant and uses a neural network acting as an adaptive controller to improve the performance of a conventional non-adaptive feedback controller. The non-collocated control of a flexible link is characterized as a non-minimum phase system, which is difficult to be controlled by most control techniques. Two different neural approaches are used in this paper to overcome this difficulty. The first approach uses a virtual redefined output as one of the inputs for the neural network and feedback controllers, while the other employs a delayed reference input signal in the feedback path and a tapped-delay line to process the reference input before presenting it to the neural network. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/SBRN.2000.889751 | SBRN |
Keywords | Field | DocType |
before present,feedback,control systems,neural networks,adaptive control,mathematical model,inverse dynamics,inverse problems,error correction,aerodynamics,neural network,signal processing | Control theory,Feedback controller,Control theory,Computer science,Networked control system,Time delay neural network,Adaptive control,Inverse dynamics,Artificial neural network,Feed forward | Conference |
ISBN | Citations | PageRank |
0-7695-0856-1 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Areolino de Almeida Neto | 1 | 5 | 2.84 |
| Wilson Rios Neto | 2 | 0 | 0.34 |
| Luiz Carlos Sandoval Góes | 3 | 22 | 3.25 |
| Cairo L. Nascimento Jr. | 4 | 9 | 3.08 |