Neural network control for large scale systems with faults and perturbations

This paper provides an adaptation algorithm for the control of complex system via recurrent neural networks. The proposed method is derived from RTRL algorithm. Neural emulator and neural controller parameters are one-line updated independently. To illustrate the tracking and the disturbance rejection capabilities of the real time control algorithm and the efficiency of the networks parameters relaxation, an application to the large scale process: Tennessee Eastman Challenge Process (TECP) is presented.