Nonlinear predictive control of periodically forced chemical reactors

Abstract

A nonlinear model-predictive control strategy is developed to maintain the superior-to-steady-state performance of a periodically forced chemical reactor. The performance of the predictive controller is investigated in the presence of measurement disturbances and parametric uncertainty. It is also shown that statistically inferred input-output models can be a substitute whenever detailed fundamental models are not available. A nonlinear autoregressive polynomial model based on observed plant data is built and incorporated into the control scheme, The catalytic oxidation of ethylene in a periodically-forced, continuous stirred-tank reactor is considered as the test case. Recently, Sinha et al. (1990) developed an adaptive control scheme for controlling nonlinear systems that exhibit complex periodic behavior. They use the difference between the actual