Abstract
Every process is having uncertainty, affects the performance of the system, even breakdown. Hence the uncertainty has to be analyzed seriously. In this paper, the parametric uncertainty is determined using multiple experiment method for a highly non linear plant. An unstable bioreactor is considered as a nonlinear process for the analysis. It is having various uncertainties in the process parameter. Substrate feed and surrounding temperature are the sources of external parametric uncertainty; also the specific growth rate and dilution rate are the internal parametric uncertainty. Overall uncertainty is determined using Monte Carlo method and the results were compared with the standard methods. Simulation results addresses that the effect of various uncertainties in the biomass concentration. A robust PID controller was designed for both stable and unstable conditions using Particle swarm optimization (PSO) algorithm, which control the system effectively when the uncertain parameter varies within the specified range. Controller performances were analyzed for various uncertainties range using MATLAB Simulink and estimated the sensitivity of each parameter individually and combined.
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Babu, T., Pappa, N. (2013). Uncertainty Quantification and Sensitivity Analysis for a Nonlinear Bioreactor. In: Unnikrishnan, S., Surve, S., Bhoir, D. (eds) Advances in Computing, Communication, and Control. ICAC3 2013. Communications in Computer and Information Science, vol 361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36321-4_60
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DOI: https://doi.org/10.1007/978-3-642-36321-4_60
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