摘要
针对风能的最大化利用问题以及由风力机驱动的液压马达与柴油机之间的功率平衡问题,设计了一种风力辅助提水机的智能控制系统。该系统是一个时变的2输入2输出耦合系统,采用2个递归神经网络(El-man)在线调整2个PID控制器的参数、1个神经元解耦补偿器完成系统的解耦,实现了不依赖于对象模型的自适应PID解耦控制。仿真结果验证了该控制策略可行性,为进一步研究提供了参考。
Aiming at the problems involving maximum utilization for wind energy and power balance between wind turbine and diesel engine, a novel control scheme for pumping water aided by wind power is proposed. It is a coupling two-input-two-output and time-variable system. Based on the principle of decoupling and recurrent neural network, two diagonal recurrent neural network （DR：NN） is adopted to adjust the l^arameters of two PID controllers on-time, and one nerve cell decouple compensator is adopted for de- coupling the system, which implementing non, model adaptive decouple PID control. Finally, the validity of the proposed control strategy is identified via computer simulations, and it lays the foundation for further study.
出处
《西华大学学报（自然科学版）》
CAS
2012年第1期55-58,共4页
Journal of Xihua University:Natural Science Edition
基金
西华大学重点科研基金项目(Z1120227)
关键词
风力辅助提水机
耦合
解耦控制
神经网络
pumping water machine dirven by wind power
coupling
decouple control
neural network
