Stable adaptive control of a class of nonlinearly-parametrized bioreactor processes

In this paper a stable adaptive controller design method is suggested for a class of uncertain bioreactor processes containing several types of nonlinearly-parametrized growth models. The design is based on particular parametrizations of the process nonlinearities in conjunction with suitably chosen indicator functions. Adaptive algorithms based on such parametrizations are shown to yield stable systems in which the convergence of the output errors to zero is guaranteed in all cases of process growth models. The stability analysis is based on Lyapunov's second method and suitably constructed Lyapunov functions.

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