Neural-network based robust predictive fault-tolerant control for multi-tank system

The main contribution of the paper was to propose a robust predictive fault-tolerant control scheme for a class of non-linear discrete-time systems that can be described with the LPV models using neural networks. Indeed, the contribution can be divided into a few important points: extension of the efficient predictive control to the robust case with exogenous external disturbances acting on the system, development of robust fault estimation and compensation scheme, and an integration of the developed schemes within a unified robust predictive fault-tolerant control framework. The proposed approach was applied to the benchmark example of the multi-tank system. The achieved results show the performance of the high performance of the proposed approach. In spite of the incontestable appeal of the proposed approach there are still some points, which may further improve its effectiveness. Indeed, in the proposed approach it is assumed the the state is available and, hence a natural approach is to relax this assumption by the introduction of a suitable state estimation strategy.