ART BASED ADAPTIVE POLE PLACEMENT FOR NEUROCONTROLLERS

Indirect adaptive control of low order plants that are subjected to parametric variations arising from changes in operating environment requires real time dynamic system identification. In this paper, we propose a control scheme that utilizes a nearest neighbor search type of classifier capable of learning to dynamically identify these variations in plant parameters. The neural network architecture employed is based on the Adaptive Resonance Theory (ART-II) proposed by Carpenter and Grossberg (1987a, 1987b, 1987c, 1987d). An adaptive pole placement controller for a slow time varying linear second order system is implemented based upon this architecture to assess the performance of the network and the overall control scheme with the neural network in the control loop. The control strategy is based upon identification of changes in the time response characteristics of the system to standard test signals which are assessed by the network. A pole placement algorithm is utilized to relocate the poles of the overall closed loop system by altering the gains of the process controller to obtain desired system response. Experimental studies on a simulated system employing a Proportional Derivative controller are encouraging.