Abstract:
A hybrid control architecture is proposed integrating recurrent, dynamic neural networks into the pole placement context. The neural network topology involves a modified ...Show MoreMetadata
Abstract:
A hybrid control architecture is proposed integrating recurrent, dynamic neural networks into the pole placement context. The neural network topology involves a modified recurrent Elman network to capture the dynamics of the plant to be controlled, being the learning phase implemented on-line using a truncated backpropagation through time algorithm. At each time step the neural model, modelling a general non-linear state space system, is linearized to produce a discrete linear time varying state space model. Once the neural model is linearised some well-established standard linear control strategies can be applied. In this work the design of a decoupling pole placement controller is considered at each instant, which combined with the on-line learning of the network results in a self-tuning adaptive control scheme. Experimental results collected from a laboratory three tank system confirm the viability and cffcctivcncss of the proposed methodology.
Published in: 1999 European Control Conference (ECC)
Date of Conference: 31 August 1999 - 03 September 1999
Date Added to IEEE Xplore: 04 May 2015
Print ISBN:978-3-9524173-5-5