Abstract
AbstractAn adaptive parameter estimation algorithm for a class of biochemical processes expressed by a nonlinearly parametrized Monod's growth kinetics model is presented. Contrary to conventional least‐square or gradient‐type identification techniques, the proposed parameter estimation algorithm is developed based on Lyapunov's stability theory. A novel class of parameter‐dependent Lyapunov functions is utilized to remove the difficulty associated with estimating the unknown parameters that appear nonlinearly. A persistence of excitation (PE) condition is investigated to guarantee the convergence of the estimation scheme. Simulations are provided to verify the effectiveness of the new approach and the theoretical discussion.
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