Self-tuning control can provide desirable behaviour of a process even though the process parameters are unknown or may vary with time. Conventional self-tuning control requires that the speed of adaptation must be more rapid than that of the parameter changes. However, in practice, problems do arise when this is not the case. For example, when fault occurs in a process, the parameters may chang...

In order to increase the accuracy and improve performance of power system stabilizer (PSS) controller compared methods presented in other studies, this paper presents a new method for tuning sliding mode control (SMC) parameters PSS using deep neural network. This requires fast switching which can create unwanted signals. To solve problem, boundary layer is used. First, equations multi-machine ...

High precision control of substrate tension is the premise and guarantee for producing high-quality products in roll-to-roll coating machine. However, complex relationships system make problems decoupling difficult to be solved, which has limited improvement accuracy Therefore, an ADRC parameters self-tuning strategy based on RBF neural network proposed improve this paper. Firstly, a global cou...

This paper deals with the design of single fuzzy neural network (SFNN) with sliding mode control (SMC) systems by using the approach of sliding mode control. The fuzzy sliding mode control (FSMC) in the system guarantee the state can reach the user-defined surface in finite time and then it slides into the origin along the surface. The key idea is to apply parameters of the membership function ...