将学习理论与鲁棒控制相结合，采用随机化算法针对实参数不确定系统讨论了鲁棒控制器的设计问题。
This paper combines learning theory with robust control and discusses robust control design problems involving real parameter uncertainty in control systems based on randomized algorithms.
基于概率鲁棒方法，针对具有实参数不确定性的多输入多输出被控对象，提出一种分散PID控制器设计方法。
A tuning method for decentralized PID controllers was developed based on probabilistic robustness for use in multi-input-multi-output plants with varying parameters.
在此基础上提出了采用遗传算法对压实参数模糊控制规则进行优化的新思路。
We also propose novel ideas in optimizing the fuzzy control rules, using genetic algorithms.
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