准确、快速地识别出车辆当前行驶的路面激励信息，是实现智能底盘控制进而保证车辆平顺性的关键。针对传统路面不平度识别算法准确率低、自适应性差等问题，提出了基于遗传算法(genetic algorithm，GA)优化长短期记忆神经网络(long short-term memory networks,LSTM)自适应卡尔曼滤波的路面不平度识别算法。基于2自由度车辆悬架模型，通过灰色关联法选择LSTM神经网络的特征输入变量，并采用GA优化LSTM神经网络的模型参数以准确识别路面等级，并据此实时更新卡尔曼滤波器算法中的噪声矩阵，实现了在复杂路况下对路面不平度的自适应识别。仿真和实验研究表明，所提出的基于GA-LSTM自适应卡尔曼滤波算法能够快速准确的识别路面不平度与路面等级，与传统卡尔曼滤波算法相比，相关系数、均方根误差和最大绝对误差分别提高3.11%、37.5%和51.2%，表明所提算法对复杂工况具有很好的自适应能力。
Abstract
Accurately and rapidly identifying the current road excitation information of the vehicle is the key to realize the intelligent chassis control and ensure the ride comfort of the vehicle. Aiming at the problems of low accuracy and poor adaptability of traditional road roughness recognition algorithms, a road roughness recognition algorithm based on genetic algorithm(GA)optimized long short-term memory neural network(LSTM)adaptive Kalman filtering was proposed. Based on the two-degree-of-freedom vehicle suspension model, the feature input variables of the LSTM neural network were selected by the grey correlation method, and the model parameters of the LSTM neural network were optimized by GA to accurately identify the road grade. Based on this, the noise matrix in the Kalman filtering algorithm is updated in real time, and the adaptive recognition of road roughness under complex road conditions is realized. Simulation and experimental results show that the proposed adaptive Kalman filtering algorithm with GA-LSTM can quickly and accurately identify road roughness and road grade. Compared with the traditional Kalman filtering algorithm, The correlation coefficient, root mean square error and maximum absolute error are increased by 3.11 %, 37.5 % and 51.2 %, respectively, indicating that the proposed algorithm has good adaptability to complex working conditions.
关键词
路面不平度识别 /
自适应卡尔曼滤波器 /
GA-LSTM /
灰色关联法
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Key words
road roughness identification /
adaptive Kalman filtering /
GA-LSTM /
grey relational analysis
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