基于T-S模糊模型的间歇过程的迭代学习容错控制

间歇过程不仅具有强非线性，同时还会受到诸如执行器等故障影响，研究非线性间歇过程在具有故障的情况下依然稳定运行至关重要。针对执行器增益故障及系统所具有的强非线性，提出一种新的基于间歇过程的T-S模糊模型的复合迭代学习容错控制方法。首先根据间歇过程的非线性模型，利用扇区非线性方法建立其T-S模糊故障模型，再利用间歇过程的二维特性与重复特性，在2D系统理论框架内，设计2D复合ILC容错控制器，进而构建此T-S模糊模型的等价二维Rosser模型，接着利用Lyapunov方法给出系统稳定充分条件并求解控制器增益。针对强非线性的连续搅拌釜进行仿真，结果表明所提出方法具有可行性与有效性。

Iterative learning fault-tolerant control for batch processes based on T-S fuzzy model

Batch processes are not with highly nonlinearity, but also suffer from the actuator failures. Study of the stability of nonlinear batch processes under failure conditions is of great significance. With considering on the actuator gain faults and the highly nonlinearity, a new T-S fuzzy model-based iterative learning fault-tolerant control method is proposed for nonlinear batch process. Firstly, the T-S fuzzy model is employed to represent the nonlinear batch process. Then a 2D compound iterative learning fault-tolerant controller is proposed by exploiting the 2D and repetitive nature of batch processes, and the equivalent 2D Rosser model of the fuzzy model is constructed. Lastly, the sufficient condition guaranteeing the system stable is given through a Lyapunov function method, and the controller gains are designed in terms of linear matrix inequalities (LMIs). Simulation to a highly nonlinear continuous stirred tank reactor (CSTR) demonstrates the feasibility and efficiency of the proposed method.