An Efficient Iterative Learning Predictive Functional Control for Nonlinear Batch Processes

Iterative learning model-predictive control (ILMPC) is very 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 challenging due to the difficulty in balancing the computational efficiency and tracking accuracy. In this article, an efficient iterative learning predictive functional control (ILPFC) is proposed. The original nonlinear system is linearized along the reference trajectory to derive a 2-D tracking-error predictive model. The linearization error is compensated by utilizing the Lipschitz condition so that the objective function can be formulated with the upper bound of the actual tracking error. For enhancing control efficiency, predictive functional control (PFC) is applied in the time domain, which reduces the dimension of the decision variable in order to effectively cut down the computational burden. The stability and convergence of this ILPFC with terminal constraint are analyzed theoretically. Simulations on an unmanned ground vehicle and a typical fast batch reactor verify the effectiveness of the proposed control algorithm.

Automated summary

Iterative learning predictive functional control (ILPFC) is an efficient control method for fast batch processes with strong nonlinear dynamics. It linearizes the system along a reference trajectory and compensates for the linearization error using the Lipschitz condition, reducing the computational burden. Predictive functional control is applied in the time domain to enhance control efficiency. The stability and convergence of ILPFC with terminal constraint are theoretically analyzed, and simulations verify the effectiveness of the proposed control algorithm.