Adaptive Extremum Seeking Control of Nonisothermal Continuous Stirred Tank Reactors with Temperature Constraints

In this paper, we present an adaptive extremum seeking control scheme for nonisothermal continuous stirred tank reactors subject to reactor temperature constraints. Only limited knowledge of the reaction kinetics is assumed with no direct measurement of the reaction mixture composition. An adaptive learning technique is introduced to construct an optimum seeking algorithm that drives the system states to optimal equilibrium concentrations of the reaction mixture taking into account reactor temperature constraints. Lyapunov’s stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. Under mild assumptions, the resulting controller is an output-feedback controller. The performance of the technique is demonstrated with the van de Vusse reaction.