燃料电池发动机优化控制的建模与蚂蚁算法研究

以某燃料电池发动机为原型,将神经网络辨识方法应用到其非线性系统的建模中.仿真结果表明,方法可行,建立的模型精度较高.在此模型的基础上,为了缩短发动机在常温启动时的暖机时间(升温至正常工作温度范围)和提高其输出功率以及在高温怠速加速时,保持其升温的平稳性和缩短达到额定输出功率的时间,将蚂蚁算法应用于发动机的优化控制问题.最后通过对模型的测试,采用蚂蚁算法优化后的控制方法基本达到了要求目标,与传统PID控制方法相比较,显示了其优越性.

Modeling of fuel cell engine and application of ant algorithm to its optimized controlling

The model of a real FCE(fuel cell engine) is set up by using neural network identification.The validity and accuracy of the model are proved by the simulation results.Then based on the model.In order to shorten the time of warming machine and improve the poweroutput of FCE after cold start as well as to raise temperature smoothly from idle to accelerating state and reach the rating poweroutput as soon as possible,a new method for solving the optimized control of FCE problem is proposed by using the idea of ant colony.The model test results show that it basically achieves the goal and show the advantage through comparing with traditional PID method.