Network-Based Fuzzy Control for Nonlinear Industrial Processes with Predictive Compensation Strategy

In this paper, the output feedback control problem is investigated for general nonlinear industrial processes. At the device layer, the nonlinear industrial processes with disturbances are modeled by utilizing Takagi-Sugeno modeling approach, and the corresponding local controllers are then designed to guarantee that the outputs for the local subsystems can track the decomposed setpoints. At the operation layer, considering the effect of radial basis function performance index and packet dropout phenomenon, a setpoint compensator is constructed to dynamically regulate the setpoints and track the given operation index. Finally, a network-based continuous stirred tank reactor system is considered to verify the validity of the proposed strategy in the simulation part.