Application of Wiener model predictive control (WMPC) to a pH neutralization experiment

pH control is recognized as an industrially important, yet notoriously diff icult control problem, Wiener models, consisting of a linear dynamic elemen t followed in series by a static nonlinear element, are considered to be id eal for representing this and several other nonlinear processes. Wiener mod els require little more effort in development than a standard Linear step-r esponse model, yet offer superior characterization of systems with highly n onlinear gains. These models may be incorporated into model predictive cont rol (MPC) schemes in a unique way which effectively removes the nonlinearit y from the control problem, preserving many of the favorable properties of linear MPC. In this paper, Wiener model predictive control (WMPC) is evalua ted experimentally, and also compared with benchmark proportional integral derivative (PID) and linear MPC strategies, considering the effects of outp ut constraints and modeling error.