A novel approach to constrained generic model control based on quadratic programming

The ability to handle constraints is a very important feature for process c ontrol algorithms. The conventional generic model control (GMC) uses genera l nonlinear programming to handle the constraints, which limits its industr ial implementation. In this paper, after introducing an approximate model-b ased predictor, we present a quadratic programming-based optimization algor ithm, which has the ability to handle linear constraints of manipulated and controlled variables and their moving velocities. By combination of the pr oposed optimization algorithm with the generic model control scheme, a nove l approach to constrained generic model control based on quadratic programm ing is proposed for nonlinear affine systems with relative order 1. Compute r simulation results show that the proposed approach has definite robustnes s against process/model parameter mismatches, it can be applied in real tim e, and it appears to hold a considerable promise in process control.