Adaptive Sliding Mode and Fuzzy Gain Scheduling Control for Steam Temperature in Power Plants

The paper presents a novel adaptive sliding mode fuzzy control strategy (AFSMC) for maintaining steam temperature in power plants. The AFSMC consists of a learning algorithm inspired by the sliding-mode control (SMC) and fuzzy gain scheduling integral control to compensate system offsets. The performance index for the learning algorithm has a similar structure to the switching variables derived from a sliding mode control and a gradient function is formed to minimise the performance index for achieving optimal control. The AFSMC design does not require the detailed system information and can work in adaptation in a variable environment. This new control strategy is applied to superheated steam temperature cascade control and it can also be applied to other nonlinear systems. Simulation results show that the controlled system has achieved not only satisfactory the requirement in stability, accuracy and response speed but also has a great robust and smooth control performance within a wide range of operation conditions.