A new extended prediction self-adaptive control (EPSAC) strategy for batch control

Abstract

It is well documented that linear model predictive control (MPC) techniques are predominantly used in industrial applications1. Driven by the stringent specifications on product quality, tighter environmental regulation of effluent streams, and higher competition in the process industries, the development of nonlinear model predictive control (NMPC) techniques is of interest to both the academic and industrial sectors. The main benefit of NMPC lies in its capability to handle nonlinearities and time-varying characteristics inherent in process dynamics while performing real-time dynamic optimization with constraints and bounds on both system states and manipulated variables. Toward this end, various methods have been employed to approximate the process nonlinearity in the various NMPC design methods reported in the literature, including successive linearization4, neural networks5, multiple local models, piecewise linearization8, and hybrid models.