为了满足预见性巡航控制(predictive cruise control,PCC)系统对重型卡车质量的精度要求,针对传统重型卡车质量估计算法的不足,设计了重型卡车的质量估计系统。开发了基于车辆纵向动力学和基于高精度地图的卡车质量估算策略,采用归一化...为了满足预见性巡航控制(predictive cruise control,PCC)系统对重型卡车质量的精度要求,针对传统重型卡车质量估计算法的不足,设计了重型卡车的质量估计系统。开发了基于车辆纵向动力学和基于高精度地图的卡车质量估算策略,采用归一化最小均方(normalized least mean square,NLMS)算法对估计质量进行了平滑性处理;完成了质量估计系统的硬件设计;搭建了质量估计算法的Simulink模型,采用基于模型设计的方法进行了系统软件的开发;实车验证了整个系统的可靠性以及质量估计算法的精确性。试验结果表明:与实际的卡车质量相比,质量估计系统计算得到的卡车质量的误差在9%以内。展开更多
A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization f...A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.展开更多
文摘为了满足预见性巡航控制(predictive cruise control,PCC)系统对重型卡车质量的精度要求,针对传统重型卡车质量估计算法的不足,设计了重型卡车的质量估计系统。开发了基于车辆纵向动力学和基于高精度地图的卡车质量估算策略,采用归一化最小均方(normalized least mean square,NLMS)算法对估计质量进行了平滑性处理;完成了质量估计系统的硬件设计;搭建了质量估计算法的Simulink模型,采用基于模型设计的方法进行了系统软件的开发;实车验证了整个系统的可靠性以及质量估计算法的精确性。试验结果表明:与实际的卡车质量相比,质量估计系统计算得到的卡车质量的误差在9%以内。
基金supported by the National Natural Science Foundation of China(61571131 11604055)
文摘A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.
