Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China.
Department of Electrical and Computer Engineering, Baylor University, One Bear Place #97356, Waco, TX 76798-7356, USA.
ISA Trans. 2015 May;56:241-51. doi: 10.1016/j.isatra.2014.11.018. Epub 2014 Dec 18.
This paper develops a stable fuzzy model predictive controller (SFMPC) to solve the superheater steam temperature (SST) control problem in a power plant. First, a data-driven Takagi-Sugeno (TS) fuzzy model is developed to approximate the behavior of the SST control system using the subspace identification (SID) method. Then, an SFMPC for output regulation is designed based on the TS-fuzzy model to regulate the SST while guaranteeing the input-to-state stability under the input constraints. The effect of modeling mismatches and unknown plant behavior variations are overcome by the use of a disturbance term and steady-state target calculator (SSTC). Simulation results for a 600 MW power plant show that an offset-free tracking of SST can be achieved over a wide range of load variation.
本文开发了一种稳定的模糊模型预测控制器 (SFMPC)，以解决电厂过热汽温 (SST) 控制问题。首先，使用子空间辨识 (SID) 方法，基于数据驱动的 Takagi-Sugeno (TS) 模糊模型来近似 SST 控制系统的行为。然后，基于 TS-模糊模型设计了用于输出调节的 SFMPC，以在输入约束下调节 SST，同时保证输入到状态稳定性。通过使用干扰项和稳态目标计算器 (SSTC)，克服了建模不匹配和未知的植物行为变化的影响。对于 600MW 电厂的仿真结果表明，在大范围的负荷变化下，可以实现无偏跟踪 SST。
