Adaptive Linearizing Control of a Nonlinear Chemical Process Based on Reduced Design Model

In this paper, a control synthesis method requiring the least knowledge on a given process, such as the available outputs and a parametrized reduced design model, is presented. The reduced design model induced by the state transformation including independent output functions is described in the output variables as the transformed state and its minimal dimension is equal to the relative order of the system. It also has parametric uncertainties mainly resulting from the couplings with the disturbance model, which comprises the rest of the transformed system but is excluded from the design work. The method referred to as the ALIC (adaptive linearizing integral control) is a combination of the time-variant input-output linearization with generic model control-like parameter estimation for asymptotic linearization of the parametric design model. Thus, the method does not need the full state availability and the necessity of exact cancellations of nonlinear terms known as critical weak points of the state feedback linearization. Its applicability and effectiveness are demonstrated by simulation for typical control problems in the chemical processes. And, the simplicity and the clarity of the design procedure are elucidated.