Data-driven optimal PID type ILC for a class of nonlinear batch process

Abstract

The paper presents model-free proportional–integral–derivative (PID) type iterative learning control (ILC) approach for the nonlinear batch process. The dynamic linearisation method is considered, which uses the input-output (I/O) measurements to update the model at each iteration. Based on the newly updated model and error information of the previous iteration, optimal PID gains are updated iteratively. The quadratic performance index is employed to optimise the parameters of the PID controller, and then an optimal PID type data-driven iterative learning control (DDILC) scheme is established for nonlinear batch process. The convergence analysis of optimal PID type DDILC is also discussed which can be enhanced by the proper choice of penalty matrices. Simulation examples are also given to demonstrate the effectiveness of the proposed scheme.