Adaptive control of track tension estimation using radial basis function neural network
Adaptive control of track tension estimation using radial basis function neural network
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DOI：
10.1016/j.dt.2020.07.011
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发表时间：
2020-08
影响因子：
5.1
通讯作者：
Pingxin Wang;X. Rui;Hailong Yu;Guoping Wang;Dong-yang Chen
中科院分区：
文献类型：
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作者：
Pingxin Wang;X. Rui;Hailong Yu;Guoping Wang;Dong-yang Chen
轨道张力是影响轨道可靠性的主要因素。为了降低轨道剥离的风险，有必要保持轨道张力恒定。然而，在越野操作期间难以测量动态张力。通过对托辊、支重臂、行走轮和支重臂的自由体关系和外力的分析，建立了履带张力的理论估算模型。将估算结果与多体动力学仿真结果进行比较，验证了轨道张力监测的合理性。在此基础上，设计了一种履带张力控制系统，包括基于径向基函数神经网络的自整定PID控制器，一个电-仿真结果表明，该控制器能快速达到不同的期望张力，准确地与传统的PID控制器相比，该控制器通过在线修正控制参数，具有更强的抗干扰能力。
Track tension is a major factor influencing the reliability of a track. In order to reduce the risk of track peel-off, it is necessary to keep track tension constant. However, it is difficult to measure the dynamic tension during off-road operation. Based on the analysis of the relation and external forces depending on free body diagrams of the idler, idler arm, road wheel and road arm, a theoretical estimation model of track tension is built. Comparing estimation results with multibody dynamics simulation results, the rationality of track tension monitor is validated. By the aid of this monitor, a track tension control system is designed, which includes a self-tuning proportional-integral-derivative (PID) controller based on radial basis function neural network, an electro-hydraulic servo system and an idler arm. The tightness of track can be adjusted by turning the idler arm. Simulation results of the vehicle starting process indicate that the controller can reach different expected tensions quickly and accurately. Compared with a traditional PID controller, the proposed controller has a stronger anti-disturbance ability by amending control parameters online.