加热炉炉温的模糊广义预测控制仿真研究

Simulation Study of Fuzzy Generalized Predictive Control for Reheating Furnace Temperature

针对加热炉炉温的大惯性、大滞后及非线性等特点,提出一种基于T-S模糊模型的模糊广义预测控制策略。T-S模糊模型的前件和后件参数分别采用粒子群优化的模糊C-均值算法和递推最小二乘法辨识,根据输入变量更新模型隶属度并将T-S模糊模型等价转换为线性模型,以此作为预测模型应用于广义预测控制。仿真结果表明:该方法在不同工况下均具有较短的调节时间,在扰动作用下有很强的鲁棒性。

Considering the great inertia, large lag and nonlinearity of the heating furnace＇ s temperature, a T- S fuzzy model-based fuzzy generalized predictive control strategy was proposed, and making use of PSO-optimized fuzzy C-means （FCM） algorithm and recursive least squares method to identify antecedent and consequent parameters of the T-S fuzzy model respectively was implemented, including having membership of updated model of the input variables based to transform the fuzzy model equivalently into a linearized model at each sampling point, and then having it taken as prediction model and applied to the generalized predictive control. The simulation results show that, the proposed method has shorter setting time under different operating conditions and has strong robustness under disturbance.