Iterative learning hybrid robust predictive fault-tolerant control for nonlinear batch processes with partial actuator faults

Abstract

For nonlinear batch processes with uncertainties, disturbances and partial actuator faults, an iterative learning robust predictive fault-tolerant control approach is developed. Based on the conclusion of robustness analysis, the stability conditions combining the robust positive invariant set with the terminal constraint set can guarantee that the system is stable under faults. Furthermore, the real-time optimal gains of control law solved online can significantly reduce the learning cycles of the controller, which in turn can meet the demand for rapid production of batch processes. The injection process is employed as an example to ultimately confirm the viability and efficacy of the developed approach.