Multivariable Model Predictive Control for Integrating Processes with Input Constraints

The model predictive control (MPC) strategy with input constraints may lead to infeasibility of the control algorithm in short term and degradation of the control performance. For the control of integrating processes, the system input constraints will be possible to come into conflict with the constraints caused by zeroing the integrating modes of the system at the end of the control horizon. In order to deal with this problem and increase the feasibility, the effect of setpoint on feasibility is studied for multivariable model predictive control of integrating processes in this paper. An improved algorithm is proposed by recalculating the setpoints according to the hard constraints before calculating the manipulated variable. The simulation results verify the efficiency and feasibility.