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高品质控制系统应该具备稳、准、快特点,本文研究了多工况下复杂系统的高品质控制问题.首先,用模型族覆盖系统在不同工况下的动态行为,然后,以后验概率为协调变量,提出了多模型学习与控制优化算法,证明了在稳态情况下用本文方法设计的控制律等同于真实模型的控制律,同时,控制器不需要在多个模型间进行硬切换,是一种软切换策略,最后通过对两个例子的仿真,说明了算法的有效性.
The high quality control system should have the characteristics of stability, accuracy and quickness.This paper studies the problem of high quality control of complex system under multi-working conditions.First, the dynamic behavior of the system is covered by the model family under different working conditions, and then the posterior probability Coordination variables are proposed and multi-model learning and control optimization algorithms are proposed. The control law of the control law equivalent to the real model under steady-state conditions is proved. At the same time, the controller does not need hard switching among multiple models, Is a soft handover strategy. Finally, the simulation of two examples shows the effectiveness of the algorithm.