利用小波分析和云模型实现机车优化粘着控制*

分析了电力机车粘着控制系统的基本原理，建立了包含粘着特性的机车牵引力传递模型；利用小波分析消除车轮速度信号中干扰噪声，提高机车空转趋势识别的可靠性。云模型将模糊性和随机性有机地结合在一起，实现定性概念和定量表示间的转换;针对机车牵引系统的强非线性和不确定性，设计了云模型粘着控制器。通过与传统的粘着控制方法对比表明，小波分析和云模型粘着控制方法不但可以有效抑制空转，同时可以实现优化粘着控制。

Using wavelet analysis and cloud model to optimized adhesion control of electric locomotives

This paper analyzed the principal theory of adhesion control system for the electric locomotives. Developed the simulative model of the locomotive traction system consideringthe characteristics of adhesion, denoised denoised the circumferential velocity of the wheel using wavelet analysis. The trendof wheel slipping could be identifiedreliably. The cloud model integrated the fuzziness and randomness and could achieve the transformation between qualitative and quantitative knowledge.Using cloud model method, designed the anti-slip re-adhesion controller to solve the nonlinear and uncertainties of the locomotive traction system. The proposed adhesion controller contrasted with conventional methods, can suppressthe slipping of the wheel efficientlyand realize optimized readhesion control.