复杂系统多模型学习与控制优化设计方法

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.

Multiple model learn and control optimization design method for complex systems

High quality control system should have stability, accuracy and rapidity features. This paper studies the high quality control problem for the complex systems under the multiple-model condition. Firstly, the systems' dynamic behavior with different conditions is described using a bank of models. Secondly, regarding the posteriori probabilities as the coordinate variables, multiple model learning and optimization control algorithm is proposed, which proves that the control law proposed in this paper is equivalent to the control law with the real model under the condition of steady state. At the same time, the controller does not need to switch toughly among multiple models. In other words, it is soft switching strategy. Finally, the two simulation results illustrate the effectiveness of the algorithm.