A
control system simultaneously controls a multi-zone process with a self-adaptive model
predictive controller (MPC), such as
temperature control within a
plastic injection molding system. The controller is initialized with basic
system information. A pre-identification procedure determines a suggested
system sampling rate, delays or “dead times” for each zone and initial
system model matrix coefficients necessary for operation of the control predictions. The recursive
least squares based
system model update,
control variable predictions and calculations of the control
horizon values are preferably executed in real time by using matrix calculation basic functions implemented and optimized for being used in a S7 environment by a Siemens PLC. The number of predictions and the
horizon of the control steps required to achieve the
setpoint are significantly high to achieve smooth and
robust control. Several matrix calculations, including an inverse matrix procedure performed at each sample pulse and for each individual zone determine the MPC
gain matrices needed to bring the system with minimum control effort and variations to the final
setpoint. Corrective signals, based on the predictive model and the minimization criteria explained above, are issued to adjust system heating / cooling outputs at the next sample time occurrence, so as to bring the system to the desired
set point. The process is repeated continuously at each sample pulse.