Nonlinear model-based predictive control of control nonaffine systems

Abstract

This paper proposes a new nonlinear model-based predictive control (NLMPC) algorithm, designed to handle control nonaffine systems (nonlinear in the manipulated variable), which is based on a reinterpretation of the prediction equation as a Taylor series expansion. They key feature of this algorithm lies in the use of a process output prediction that accounts for changes in process dynamics as a function of the operating point as well as of the magnitude of the process input change. From this algorithm, a suboptimal NLMPC algorithm is derived to improve computational efficiency for real-time applications. The performance improvement of the new algorithm is illustrated by comparing it with a standard nonlinear model-based predictive control algorithm using three examples. The first two examples are simulations of simple static nonlinear systems, and the third example is a simulation of a semibatch acrylonitrile-butadiene (NBR) emulsion polymerization process.