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 SAN-hybrid controller is tested for simulated fed-batch production of methionine for three different process conditions. Simulations assume that online measurements of dissolved oxygen (DO) concentration are available. The performance of the SAN-hybrid controller gave an NRMSE of similar to 10(-4) in the absence of noise, similar to 10(-3) and similar to 10(-2) for +/- 5% and +/- 10% noise in the DO measurement and similar to 10(-2) 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 SAN-hybrid 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 SAN-hybrid controller can be used for online real-time implementation in fed-batch bioprocesses. (C) 2009 Society of Chemical Industry