Application of the state space neural network to the fault tolerant control system of the PLC-controlled laboratory stand

This paper deals with the design of a fault tolerant control system for a laboratory stand. With the application of a state space neural network it is possible to design both the nonlinear model and the observer of the considered plant. Analysing outputs of those models, it is possible to carry out fault detection. In order to cope with uncertainties of the model, a robust fault detection scheme is used which is based on the model error modelling technique. When a fault is detected, the fault tolerant control starts to compensate the fault effect. This is achieved through a proper recalculation of a control law. The new control law is obtained by adding an auxiliary signal to the standard control. This auxiliary control constitutes the additional control loop which can affect the stability of the entire control system. Therefore, stability of the proposed control scheme based on the Lyapunov direct method is also investigated. Finally, the approach is tested on the fluid flow and pressure control laboratory stand.