基于免疫遗传算法的模糊径向基函数神经网络在高速电主轴中的应用

为满足高速高性能电主轴系统快、稳、准的控制要求，结合免疫遗传算法寻优速度快及模糊神经网络控制不依赖主轴系统模型的优点，设计了一种将模糊逻辑控制、径向基函数(Radical basis function, RBF)神经网络和免疫遗传算法进行有机结合的高速电主轴系统全局优化的控制策略，并将该智能控制策略成功应用于高速电主轴系统双闭环矢量控制系统的转速控制器中。通过免疫遗传算法对该智能控制器三类参数的同步优化取得了最佳控制效果，从而实现了对主轴输出转速的精确控制。试验和仿真结果验证了所设计的控制器能够精确控制主轴的输出转速，而且当高速电主轴受到突加负载冲击时，具有很好的抗干扰性能及较强的鲁棒性，使主轴系统具有优良的动、静态性能，实现了高品质驱动。

Application of Immune Genetic Algorithm Based Fuzzy RBF Neural Network in High-speed Motorized Spindles

To satisfy the control requirements of fast, stable, accurate in the high speed and high performance motorized spindle system, a global optimization control strategy of high-speed motorized spindle which is the organic combination of fuzzy logic control, radial basis function (RBF) neural network and immune genetic algorithm is proposed. The control strategy combines the advantages of fast searching optimization of immune genetic algorithm and independence on spindle system model of fuzzy neural network, and this intelligent control strategy is successfully applied to the speed controller of double closed-loop vector control system for high-speed motorized spindle. The simultaneous optimization of the three types parameters of the intelligent controller can achieve optimal control effect through using IGA, and successfully implement the accurate speed control process. This strategy can accurately control spindle speed and perform quite good anti-interference ability and strong robustness when spindle bears instant impact load, which is verified through experimental and simulation results, and both dynamic and steady performance are improved evidently. The spindle system can achieve high-quality drive finally.