ESTIMATION AND FILTERING OF NONLINEAR-SYSTEMS - APPLICATION TO A WASTE-WATER TREATMENT PROCESS

A fundamental task in design and control of biotechnological processes is system modelling. This task is made difficult by the scarceness of online direct sensors for some key variables and by the fact that ide ntifiability of models, including the Michaelis-Menten type of nonline arities, is not straightforward. The use of adaptive estimation approa ches constitutes an interesting alternative To circumvent these kinds of problems. This paper discusses an identification technique derived to solve the problem of estimating simultaneoulsy inaccessible state v ariables and time-varying parameters of a nonlinear wastewater treatme nt process. An extended linearization technique using Kronecker's calc ulation provides the error model of the joint observer-estimator proce dure, whose convergence is proven via Lyapunov's method. Sufficient co nditions for stability of this joint identification scheme are given a nd discussed according to the persistency excitation conditions of the signals. A simulation study with measurement noises and abrupt jumps of the process parameters shows the feasability and significant robust ness of the proposed adaptive estimation methodology.