Quantized Control of Markov Jump Nonlinear Systems Based on Fuzzy Hidden Markov Model
研究了具有随机量化的非线性马尔可夫跳变系统的异步保成本控制问题，利用隐马尔可夫模型描述非同步控制器和随机量化，基于T-S模糊技术和李雅普诺夫函数方法给出了确保闭环系统渐近稳定并得到最小保成本性能上界的充分条件。
This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the nonsynchronous controller and the random quantization phenomenon. Based on Takagi-Sugeno fuzzy technique and Lyapunov function approach, a sufficient condition is obtained, which can not only ensure the asymptotic stability of the closed-loop system and existence of the desired controller, but also can yield the minimal upper bound of GCC performance. Finally, two examples are provided to demonstrate the correctness and reliability of our developed approaches.