Name
Title | ||
|---|---|---|
An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances. |
Abstract | ||
|---|---|---|
We present a recurrent neural-network (RNN) controller designed to solve the tracking problem for control systems. We demonstrate that a major difficulty in training any RNN is the problem of exploding gradients, and we propose a solution to this in the case of tracking problems, by introducing a stabilization matrix and by using carefully constrained context units. This solution allows us to achieve consistently lower training errors, and hence allows us to more easily introduce adaptive capabilities. The resulting RNN is one that has been trained off-line to be rapidly adaptive to changing plant conditions and changing tracking targets. The case study we use is a renewable-energy generator application; that of producing an efficient controller for a three-phase grid-connected converter. The controller we produce can cope with the random variation of system parameters and fluctuating grid voltages. It produces tracking control with almost instantaneous response to changing reference states, and virtually zero oscillation. This compares very favorably to the classical proportional integrator (PI) controllers, which we show produce a much slower response and settling time. In addition, the RNN we propose exhibits better learning stability and convergence properties, and can exhibit faster adaptation, than has been achieved with adaptive critic designs. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neunet.2013.09.010 | Neural Networks |
Keywords | Field | DocType |
recurrent neural networks,control system,stabilization matrix,tracking problem,slower response,efficient controller,adaptive capability,tracking target,instantaneous response,case study,vector control,adaptive critic design,lower training error,exploding gradients,predictive input,adaptive recurrent neural-network controller | Convergence (routing),Vector control,Control theory,Settling time,Control theory,Recurrent neural network,Integrator,Artificial intelligence,Control system,Grid,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
49 | 1 | 1879-2782 |
Citations | PageRank | References |
8 | 0.51 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Fairbank | 1 | 69 | 10.13 |
| Shuhui Li | 2 | 157 | 9.08 |
| Xingang Fu | 3 | 10 | 1.89 |
| Alonso, E. | 4 | 29 | 3.31 |
| Donald Wunsch | 5 | 96 | 17.68 |