pole assignment adaptive control 极点配置自适应控制
The improved GA is used to optimize the parameters of pole assignment control and LQR control about inverted pendulum systems. And the better simulation results are obtained.
运用改进遗传算法来优化极点配置方法和线性二次型调节器方法的倒立摆控制参数，并取得比较好的仿真效果。
This paper presents a class adaptive pole assignment control of servo systems based on neural state estimation and develops the system structure and the weight learning algorithms.
提出了一类基于神经元状态估计器的自适应广义极点配置控制，研究了该控制系统的网络结构和权值学习方法。
The simulation results show that the results of PID control based on GA are better than experience, but the results of pole assignment control and LQR control are not clear advantage whit experience.
仿真结果表明，基于遗传算法的PID控制效果比经验值要好，但基于遗传算法的极点配置方法和线性二次型最优控制方法的控制效果与经验值相比，没有明显的优势。
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