Optimal iterative learning control for end-point product qualities in semi-batch process based on neural network model

An optimal iterative learning control (ILC) strategy of improving endpoint products in semi-batch processes is presented by combining a neural network model. Control affine feed-forward neural network (CAFNN) is proposed to build a model of semi-batch process. The main advantage of CAFNN is to obtain analytically its gradient of endpoint products with respect to input. Therefore, an optimal ILC law with direct error feedback is obtained explicitly, and the convergence of tracking error can be analyzed theoretically. It has been proved that the tracking errors may converge to small values. The proposed modeling and control strategy is illustrated on a simulated isothermal semi-batch reactor, and the results show that the endpoint products can be improved gradually from batch to batch.