Abstract
BACKGROUND: A generalized methodology for the synthesis of a hybrid controller for affine systems using sequential adaptive networks (SAN) is presented. SAN consists of an assembly of neural networks that are ordered in a chronological sequence, with one network assigned to each sampling interval. Using a suitable process model based on oxygen metabolism and an a priori objective function, a hybrid control law is derived that can use online measurements and the states predicted by SAN for computing the desired control action.
RESULTS: The performance of the SANโhybrid controller is tested for simulated fedโbatch production of methionine for three different process conditions. Simulations assume that online measurements of dissolved oxygen (DO) concentration are available. The performance of the SANโhybrid controller gave an NRMSE of โผ10^โ4^ in the absence of noise, โผ10^โ3^ and โผ10^โ2^ for ยฑ 5% and ยฑ 10% noise in the DO measurement and โผ10^โ2^ for parameter uncertainty when compared with the ideal model prediction.
CONCLUSIONS: The observed performance for unmeasured state prediction and control implementation shows that the proposed SANโhybrid controller can efficiently compute the manipulated variable required to maintain methionine production along the optimized trajectory for different conditions. The test results show that the SANโhybrid controller can be used for online realโtime implementation in fedโbatch bioprocesses. Copyright ยฉ 2009 Society of Chemical Industry