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面对具有较强非线性、不确定性和难以建立精确数学模型的控制对象,传统PID控制很难达到理想的控制效果,而模糊控制却是一种非常有效的智能控制方法,但是常规模糊控制精度低,超调量大,同时模糊控制规则的设计存在较强的主观性,难以把握。本文采用遗传算法优化改进的变论域模糊控制中的伸缩因子,当误差较大时,将控制作用的伸缩因子乘以一个较大系数,增加控制作用,提高系统输出,迅速减小误差;当误差较小时(本文取误差小于±0.025),将伸缩因子在原有的基础上乘一个较小的系数,减少控制作用范围,使系统输出趋于平稳,从而使控制规则分布更加合理,减少对专家知识的依赖。实验结果表明,同传统PID控制策略、常规模糊控制策略和常规变论域模糊控制策略相比,无论在动态性能方面,还是在稳态性能方面,改进的变论域模糊控制策略的控制效果均优于其他3种控制策略。
In the face of the control object with strong nonlinearity, uncertainty and difficult to establish precise mathematical model, the traditional PID control is difficult to achieve the desired control effect, but fuzzy control is a very effective intelligent control method, but the conventional fuzzy control Low precision, large overshoot, fuzzy control rules at the same time there is a strong subjective design, it is difficult to grasp. In this paper, the genetic algorithm is used to optimize the scaling factor in the variable-domain fuzzy control. When the error is large, the scaling factor of the controlling effect is multiplied by a larger coefficient to increase the control effect and increase the output of the system, so as to reduce the error rapidly. When the error is small (error in this paper is less than ± 0.025), the scaling factor is multiplied by a small coefficient on the original basis to reduce the scope of control and make the output of the system stable. This will make the distribution of control rules more reasonable and reduce the knowledge of experts Dependence. The experimental results show that compared with the traditional PID control strategy, the conventional fuzzy control strategy and the conventional variable-domain fuzzy control strategy, both in the dynamic performance and in the steady-state performance, the improved control effect of the variable universe variable fuzzy control strategy Outperforms the other three control strategies.