【摘要】 为提高ATP系统的跟踪精度和快速性,将模糊控制引入单神经元自适应PID控制中,提出单神经元模糊PID控制。它运用有监督的Hebb学习规则在线修正PID参数,而神经元的比例系数则由Sugeno模糊逻辑系统根据系统的误差和误差变化量大小进行调整,使控制系统对动态过程信息的利用程度达到最优。仿真和实验结果表明,系统不仅具有自学习和自适应能力,且动态性能和稳态性能都优于经典PID控制,超调量减小0.95%;上升时间和调节时间均减小1s左右;稳态误差减小1.19′。满足现代高精度跟踪系统的需求。更多还原

【Abstract】 In order to improve the tracking precision and tracking speed, fuzzy control is introduced into single neuron PID control to form single neuron fuzzy PID control, which uses the supervising Hebb learning rules to adjust PID parameters. The proportion parameter of single neuron is adjusted based on Sugeno fuzzy logic according to error and error variation, and it can improve the application of the system dynamic process message. The results of simulation and experiment indicate that tracking system not only has the capability of learning and self-adaptation, but also have better dynamic performance and steady-state performance than that of PID control. The overshoot of system decreases by 0.95%, the setting time and the rise time decreases by 1s and the steady-state error decreases by 1.19′.更多还原