Nonlinear Model Based Control of Complex Dynamic Chemical Systems

A nonlinear internal model control (NIMC) strategy that incorporates the nonlinear model structure and the estimator dynamics in the control law is presented for the control of complex dynamical systems characterized by input-output multiplicities, nonlinear oscillations and chaos. A model based estimator is designed to provide the unmeasured process states that capture the fast changing nonlinear dynamics of the process to incorporate in the controller. The estimator uses the mathematical model of the process in conjunction with the known process measurements to estimate the states. The design and implementation of the estimator supported NIMC strategy is studied by choosing two typical continuous non-isothermal nonlinear processes, a chemical reactor and a polymerization reactor, which show rich dynamical behavior ranging from stable situations to chaos. The results evaluated under different conditions show the superior performance of the estimator based NIMC strategy over the conventional controllers for the control of complex nonlinear processes.

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