This work deals with some sliding mode (SM) and adaptive sliding-mode control (SMC) strategies for a class of nonlinear biotechnological processes. First, a dynamical SM-based feedback strategy is designed in order to ensure the asymptotic output stabilization of nonlinear bioprocesses. The control law design is done by means of a combination between the exact linearization approach and the SMC. Second, an adaptive SMC strategy is derived for this class of bioprocesses. In order to deal with the parametric uncertainties of the bioprocesses, the adaptive form of the SMC law is designed by means of direct, overparameterized adaptive control techniques available for linearizable systems. The paper also presents the implementation of the proposed control strategies for two typical bioprocesses belonging to the studied nonlinear class. The first prototype process takes place into a Continuous Stirred Tank Bioreactor, and the second is a lipase production process that takes place inside a Fed-Batch Bioreactor.

No abstract

No abstract

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

No abstract