Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise

In this paper, a reinforced gradient-type iterative learning control profile is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by external Gaussian white noise. The robustness is analyzed and the range of the step is specified by means of statistical technique and matrix theory. Compared with the conventional one, the proposed algorithm is more efficient to resist external noise. Numerical simulations of an injection molding process illustrate that the proposed scheme is feasible and effective.

An iterative learning control scheme is to iteratively generate the control signal for the next system operation by compensating for the current control signal with its proportional, derivative and/or integral tracking error(s) so that the generated sequential iterative learning control signals may drive the system to track the desired trajectory as precisely as possible as the iteration index goes to infinity. The schematic diagram and the tracking behaviors are sketched as follows.Figure optionsDownload as PowerPoint slide