An advanced multiple model based control of an industrial steam turbine using fast version of GPC

Abstract

A non-linear controller based on multiple linear models is proposed to regulate the output power of an industrial steam turbine. First, the operating regimes of the system are divided into 3 linear regions. Then, a controller auto-regressive integrated moving average (CARIMA) model is developed for each region and the general predictive control (GPC) law of its region is obtained. The linear models are used to capture the process dynamics at different operating points. The suggested 3 local linear GPC laws are utilized within a framework using the concept of non-linear multiple models. For this purpose, the nonlinear control law is built by a weighted combination of the outputs of the linear controllers. The nonlinear controller consists of three linear GPC laws which may takes too much time to be updated at each sampling time. This is not suitable for online applications. Because of this fact, a fast version of GPC is considered. The fast nonlinear GPC is acted like a weighed discrete PID controller which is updated and retuned according to set point at each sampling time. Simulated industrial steam turbine is invoked for this study under the set point tracking and load disturbance. Simulation results show the performance and effectiveness of the proposed non-linear GPC controller.