基于蚁群优化的模糊神经网络控制器的应用研究

Application Research of Fuzzy Neural Network Controller Based on Ant Colony Optimizatio

  • 摘要: 针对传统PID控制方式的不足,文章提出了一种新的永磁同步电动机控制策略,即采用蚁群优化算法对模糊神经网络控制器的3个因子参数ka、kb、ku进行全局优化,给出了永磁同步电动机的数学模型,详细介绍了模糊神经网络控制器的设计,分析了蚁群优化算法,并进行了仿真实验。仿真结果表明,基于蚁群优化模糊神经网络控制器的永磁同步电动机调速系统具有很强的鲁棒性和自适应性,动态响应快,能够较好地跟踪负载变化。

     

    Abstract: Aiming at the shortages of the traditional PID control mode,the paper proposed a new(control) strategy for permanent magnetism synchronous motor,namely using ant colony optimization(algorithm) to optimize three factors k_a,k_b,k_u of fuzzy neural network controller of overall system,and gave(mathematical) model of permanent magnetism synchronous motor,introduced design of fuzzy neural(network) controller in details,analyzed ant colony optimization algorithm and made simulation experiment at last.The simulation result showed that the speed-regulation system of permanent magnetism(synchronous) motor with fuzzy neural network controller based on ant colony optimization had strong(robustness) and adaptivity,faster dynamic response,which could better follow load variation.

     

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