摘要
基于逆动力学控制的思想,提出一种带前馈的RBFN逆模型控制策略,并将该控制策略对多变量非线性系统进行了在线解耦与控制;采用3个RBF神经网络分别对被控对象的3个输入进行逆辨识,每相神经网络逆辨识模型反向作为逆控制器模型与每相串联,从而构成3个已解耦的独立的伪线性对象,进而针对3个独立的伪线性对象进行线性控制,从而实现了对三相耦合系统的精确控制;经过实验室模拟调试的实测三相电流波形表明,在电流设定值为2100A时,升温速率为5.3℃/min,电流波动范围≤±10%。
An inverse control strategy based on RBFN with feed forward was presented in this paper. The non--linear multi--variable system was controlled by an decoupled control method. Three inputs of the plant was identified by three RBFNS. Three dynamic pseudo line- ar systems were formed through The models of controllers and the plants are in series to be decoupled into three independent pseudo--linear plant. So we can use linear method to control those plants. The actual measurement current waveforms show that the heating rate is 5.3℃/ min and current fluctuation range is≤±10% when the current set at 2100A.
出处
《计算机测量与控制》
CSCD
北大核心
2010年第7期1533-1536,1542,共5页
Computer Measurement &Control
基金
国家自然基金重大研究计划培育项目(90923029)
江苏省高校自然科学基金(08kjd560009)
淮安科技发展项目(HAG07022)
关键词
前馈
逆控制
RBFN
伪线性
解耦
feed--forward
inverse controls RBFN
dynamic pseudo linear
decouple
