Abstract: A computer method and apparatus for adaptive model predictive control (MPC) of multivariable processes is disclosed. The adaptive MPC system can perform automatic implementation for a new MPC controller, and, for an existing MPC controller, it can perform automatic maintenance when necessary. The adaptive MPC system consists of three modules: an MPC control module, an online identification module and a control monitor module. In MPC implementation, the online identification module and the MPC control module work together and perform various steps in MPC implementation automatically and efficiently. When the MPC controller is online, the control monitor module continuously monitors the MPC performance and model quality. When control performance becomes poor and considerable model degradation is detected, monitor module will start the maintenance by activating the online identification module. The identification module will re-identify the model and replace the old model.

Abstract: A computer method and apparatus of online automated model identification of multivariable processes is disclosed. The method and apparatus carries out automatically all the four basic steps of industrial process identification: 1) identification test signal design and generation, 2) identification plant test, 3) model identification and 4) model validation. During the automated plant test, process models will be automatically generated at a given time interval, for example, every hour, or on demand; the ongoing test can be automatically adjusted to meet the process constraints and to improve the data quality. Plant test can be in open loop operation, closed-loop operations or partly open loop and partly closed-loop. In a (partial) closed-loop plant test, any type of controller can be used which include proportional-integral-derivative (PID) controllers and any industrial model predictive controller (MPC).