5 years ago

Robust Iterative Learning Fault-Tolerant Control for Multiphase Batch Processes with Uncertainties

Robust Iterative Learning Fault-Tolerant Control for Multiphase Batch Processes with Uncertainties
Limin Wang, Limei Sun, Ridong Zhang, Jingxian Yu, Furong Gao
In this paper, the iterative learning fault-tolerant control problem for multiphase batch processes with uncertainty and actuator faults is studied. First, making full use of the characteristics of the two-time dimension (2D) feature and repetitiveness in batch processes and introducing the state error and output error between the adjacent batches, the established model is transformed into an equivalent 2D-Roesser switched system with different dimensions. Under the framework of the 2D system theory and by means of the average dwell time method, sufficient conditions ensuring the system to be 2D robustly stable along the time and batch directions and the minimum running time lower bound in each phase are given. Simultaneously, the designed updating law is derived. In order to examine the control performance of the proposed method, the traditional reliable control method is also investigated in this paper. The batch process is regarded as a continuous system, in which only the fault-tolerant control along the time direction is considered. Finally, the injection modeling process is taken as an example, where the main parameters, namely the injection velocity and packing-holding pressure, are controlled in the filling and packing-holding phases. The simulation results show that the proposed iterative learning fault-tolerant control method is a better choice for the multiphase batch processes with actuator faults.

Publisher URL: http://dx.doi.org/10.1021/acs.iecr.7b00525

DOI: 10.1021/acs.iecr.7b00525

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