Nonlinear adaptive control for multivariable chemical processes

Abstract

Multivariable nonlinear models are common in chemical processes. Often many of the parameters change with time and some variables are not measured. To effectively control these processes, an adaptive scheme is developed here. This scheme combines the generic model control algorithm (GMC), with a nonlinear observer, which is able to track changes in the process model. This control scheme is shown to be quite robust due to the rapid convergence of the nonlinear observer coupled with integral action introduced by the GMC controller. Disturbance rejection is achieved with feedforward action and the combined observer/controller is easy to tune. Two examples demonstrate the effectiveness of this scheme. The first example is a simulation of an exothermic batch reactor where the algorithm is used for set point trajectory tracking and calorimetric estimation. The second example is a real-time application to a laboratory pressure tank, which is effectively controlled over a wide range. Both examples illustrate the ability of this nonlinear adaptive control strategy to provide good estimation and control of these nonlinear processes.