摘要
为满足液压系统油温的高精度控制要求，提出了一套数字、比例混合式冷却系统，降低了冷却系统成本，提高了系统的可靠性．针对具有滞后特性的温度场复杂系统，提出了一种基于ＣＭＡＣ神经网络的ＣＭＡＣ预测与自学习控制算法，使油温控制精度达到５０±０．５℃，超过了ＩＳＯＡ级标准．
In order to obtain the high control precision of the oil temperature in hydraulic system,this paper proposes a scheme of digital and proportional hybrid cooling system,the cost of the cooling system is decreased and the reliability is improved. As to the complex control system of temperature field with large time delay,a CMAX prediction and self learning control algorithm based on CMAC neural network is proposed in this paper. The control precision for the oil temperature has been reached within 50±0 5 ℃,and surpassed ISO A grade.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
1997年第3期325-328,共4页
Journal of Northeastern University(Natural Science)
基金
国家教委博士点基金
