Sampled-Data-Based Iterative Cost-Learning Model Predictive Control for T–S Fuzzy Systems
针对非周期采样的非线性网络控制系统，提出了一种迭代成本学习模型预测控制方法，能保证闭环系统渐近稳定并提升迭代任务下的控制性能，通过T-S模糊系统建模和终端成本函数设计实现。
In this article, an iterative cost-learning model predictive control (ICLMPC) is proposed for nonlinear networked control systems (NCSs) in the presence of aperiodic sampling. The proposed ICLMPC is useful not only to guarantee asymptotic stability of the closed-loop system with aperiodic sampling but also to improve control performance in the case of performing an iterative task. In the proposed method, the nonlinear system of NCSs is mathematically represented as an aperiodic sampled-data Takagi–Sugeno (T–S) fuzzy system. Based on this representation, the ICLMPC design is formulated in terms of a finite-horizon optimal control problem in which a new terminal cost function is considered. The terminal cost function is constructed by a Lyapunov function with a looped-functional and an iteratively minimized function (IMF). From the Lyapunov function with the looped-functional, it is possible to guarantee that the ICLMPC asymptotically stabilizes the aperiodic sampled-data T–S fuzzy system. To obtain an iteratively improved control performance, the IMF takes the minimized value among the integrals of the collected data at each iteration. The validity and effectiveness of the proposed method are illustrated by two practical examples in the simulation section.