Ahybridneural-decouplingpole placement controller and its application

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.