Actuator fault-tolerant iterative learning control of nonlinear multi-phase batch processes
Abstract
The design of a neural-network-based fault-tolerant iterative learning control for nonlinear, multi-phase batch processes is addressed. Recurrent neural networks are used to model each phase of a batch process. The learning controller is developed using iterative procedures to solve an optimal control problem. Sufficient conditions are provided for the convergence of the tracking error and the control signal. The efficiency of the developed control scheme is illustrated by a non-trivial example of tracking control for a multi-phase injection molding process affected by repetitive and non-repetitive disturbances and/or actuator faults.
Fields of Study & Subjects
Iterative Learning Control Systems
Piezoelectric Actuators and Control
Adaptive Dynamic Programming Control
Publication details
- Journal / source
- European Journal of Control
- Publisher
- Elsevier BV
- Volume
- 90
- Pages
- 101530–101530
- Publication date
- 19 May 2026
- Language
- EN