Fault-tolerant iterative learning control for batch processes with time-varying state delays and uncertainties
Hua Chen, Rui Xu, Yao Tang, Xinyuan Long
Abstract
A proportional-derivative (PD)-type iterative learning control (ILC) combined with fault-tolerant control (FTC) for a discrete-time batch process with time-varying delays and uncertainties is presented in this paper. The main goals of paper are to drive the considered output profile to the desired reference with the avoidance of possible process faults, delays and uncertainties. First, the nonlinear estimation problem is simplified to the linear one by introducing an appropriate system reparametrisation to deal with the problem of the simultaneous estimation of state and process fault. Second, to solve the problem of time-varying delays and uncertainties, this paper makes use of the two-dimensional (2-D) and repetitive characteristics of batch processes and introduces the state error, tracking error and estimation error to establish the dynamics model of FTILC that is eventually converted into a linear repetitive process model. Then, sufficient conditions based on the Lyapunov–Krasovskii theory and H∞ methodology are established to ensure the robust stability along the trial in according with linear matrix inequalities (LMIs) for all uncertainties. Finally, this paper manifests the effectiveness of the proposed sufficient conditions by a simulation of injection moulding process.