电测与仪表
電測與儀錶
전측여의표
ELECTRICAL MEASUREMENT & INSTRUMENTATION
2014年
5期
41-45
,共5页
静止变频电源%自适应粒子群算法%模糊神经网络%单神经元%PID控制
靜止變頻電源%自適應粒子群算法%模糊神經網絡%單神經元%PID控製
정지변빈전원%자괄응입자군산법%모호신경망락%단신경원%PID공제
static inverter%APSO%fuzzy neural network%single neuron%PID control
为了提高静止变频电源输出的电压波形质量，增强控制系统的鲁棒性，提出了基于自适应粒子群优化算法( APSO)优化模糊神经PID控制策略。利用改进的自适应粒子群优化算法优化模糊神经网络的前件、后件参数和单神经元优化PID参数，实现了控制器参数的自动调整。在MATLAB/SIMULINK环境下，对该策略控制下的静止变频电源控制电路进行了仿真。结果表明，与普通的模糊神经网络PID控制对比，引入改进的粒子群优化算法可以实现参数的全局快速寻优。优化后的模糊神经PID控制器具有良好的控制性能和自适应能力，很好地满足了系统的鲁棒性、快速性的要求。
為瞭提高靜止變頻電源輸齣的電壓波形質量，增彊控製繫統的魯棒性，提齣瞭基于自適應粒子群優化算法( APSO)優化模糊神經PID控製策略。利用改進的自適應粒子群優化算法優化模糊神經網絡的前件、後件參數和單神經元優化PID參數，實現瞭控製器參數的自動調整。在MATLAB/SIMULINK環境下，對該策略控製下的靜止變頻電源控製電路進行瞭倣真。結果錶明，與普通的模糊神經網絡PID控製對比，引入改進的粒子群優化算法可以實現參數的全跼快速尋優。優化後的模糊神經PID控製器具有良好的控製性能和自適應能力，很好地滿足瞭繫統的魯棒性、快速性的要求。
위료제고정지변빈전원수출적전압파형질량，증강공제계통적로봉성，제출료기우자괄응입자군우화산법( APSO)우화모호신경PID공제책략。이용개진적자괄응입자군우화산법우화모호신경망락적전건、후건삼수화단신경원우화PID삼수，실현료공제기삼수적자동조정。재MATLAB/SIMULINK배경하，대해책략공제하적정지변빈전원공제전로진행료방진。결과표명，여보통적모호신경망락PID공제대비，인입개진적입자군우화산법가이실현삼수적전국쾌속심우。우화후적모호신경PID공제기구유량호적공제성능화자괄응능력，흔호지만족료계통적로봉성、쾌속성적요구。
In order to improve the output-voltage performance and robustness of static inverter power supply, a method of fuzzy neural PID controller optimized by auto-adaptive particle swarm optimization algorithm ( APSO) is presented in this paper. By improved APSO algorithm which has global search characteristic, control strategy optimized fuzzy neural network parameters and the PID controller parameters are optimized by single neuron so as to realize the param-eters of the controller adjusted automatically. Based on MATLAB/SIMULINK circumstance, the system is simulated in the Static Inverter circuit. Compare with the fuzzy neural network PID control, simulation results on optimized fuzzy neural PID control show that the control system has good performance of adaptive capacity, which meets the high ro-bustness and the rapidity requirements of the system.
