Abstract
Unmeasured disturbances usually plague the processes and result in defect products in chemical plants; hence, the identification and control of the process with the presence of disturbances are important. This paper completely develops the neural network model predictive control (NNMPC) from the model design to the controller design for nonlinear MIMO processes with unmeasured disturbances. In the model design, an input-driven output neural network ARX model (NNARX) combining with a disturbance AR model, called NNARX+AR, is proposed. NNARX and AR represent the input-output characteristics without the corrupted disturbances and with disturbances respectively. The Levenberg-Marquardt algorithm for NNARX and the least squares algorithm for AR are synchronously used to train the process model. In the control design, a constrained NNMPC based on NNARX+AR via the successive quadratic programming is developed to search the optimal control actions. To demonstrate the proposed identification and predictive control strategies, a pH neutralization system with the presence of unmeasured disturbances is presented.
