A High Efficiency Iterative Learning Predictive Functional Control for Nonlinear Fast Batch Processes

Iterative learning model predictive control (ILMPC) is quite popular in controlling the batch process, since it possesses not only the learning ability along batches, but also the strong time domain tracking properties. However, for a fast batch process with strong nonlinear dynamics, the application of the ILMPC is quite challengeable due to the difficulty in balancing the computational efficiency and tracking accuracy. In this paper, an efficient iterative learning predictive functional control is proposed. The original nonlinear system is linearized along reference trajectory to formulate two-dimensional tracking-error based predictive model. The linearization error is compensated to formulate the objective function as the norm bound of the real tracking error. For enhancing control efficiency, predictive functional control is incorporated to reduce the dimension of optimized variable so as to cut down computation burden effectively. The stability and convergence of this iterative learning predictive functional control with terminal constraint are analyzed. The simulations of unmanned ground vehicle and typical fast batch reactor verify the effectiveness of the proposed control algorithm. Copyright ©2019 Acta Automatica Sinica. All rights reserved.