Improved Robust Constrained Model Predictive Control Design for Industrial Processes Under Partial Actuator Faults

Abstract

Focusing on industrial processes under uncertainties and partial actuator faults, a new robust constrained model predictive control (MPC) strategy is developed. To enhance the corresponding control performance, a new state-space model in which an extended state vector is constructed by combining the state variables and the tracking error is introduced for the proposed MPC algorithm. As a consequence, there are extra degrees of freedom for the subsequent controller design by adjusting the output tracking error and the state variables separately, and the enhanced control performance is anticipated. Note that the state variables cannot be tuned in the robust MPC design that utilizes the traditional state space model so that its control performance may be limited because of the restricted degrees of freedom. Finally, the validity of the proposed robust MPC strategy is evaluated on the injection velocity control under uncertainties and partial actuator failures.