线控转向系统的自适应高阶滑模控制
作者:
作者单位:

东北大学

作者简介:

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中图分类号:

U463.42

基金项目:

国家自然科学基金(51775103);国家重点研发计划“智能机器人”重点专项(2017YFB1300200, 2017YFB1300203)


Adaptive higher-order sliding mode control for SbW system
Author:
Affiliation:

Northeastern University

Fund Project:

Supported by National Natural Science Foundation of China (51775103);National key R & D plan

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    摘要:

    车辆线控转向(steer-by-wire,SbW)系统存在摩擦力矩及回正力矩等不确定动态特性, 难以实现精确建模与有效控制. 为此, 本文提出一种基于模糊系统的自适应高阶滑模(adaptive higher-order sliding mode,AHOSM)方法实现SbW系统的有效控制. 首先, 通过模糊逻辑系统逼近SbW系统的未建模动态, 使控制器的设计不再需要摩擦力矩及回正力矩的动力学模型. 然后, 采用高阶滑模和自适应增益技术削弱传统滑模控制器存在的抖振现象. 此外, 通过构造Lyapunov函数来设计增益自适应律补偿逼近误差和系统不确定项对控制精度的影响, 该方案不需要系统不确定项的界已知且能避免增益过估计现象. 最后, 通过稳定性分析证明了该控制器可以在有限时间内建立实际滑动模态, 数字仿真和硬件在环实验进一步验证了该控制方法的有效性和优越性.

    Abstract:

    Uncertain dynamic model of friction torque and self-aligning torque in the steer-by-wire (SbW) system make it difficult to achieve accurate modeling and effective control. For this purpose, an adaptive higher-order sliding mode (AHOSM) control method based on fuzzy logic system (FLS) is proposed to control the SbW system effectively. Firstly, the FLS is employed to approximate the unmodeled dynamics of the SbW system, so that the dynamic models of the friction torque and self-aligning torque are no longer needed in the design of the controller. Then, the higher-order sliding mode and dynamical gain technique are introduced to reduce chattering in traditional sliding mode controllers. In addition, the gain adaptive law obtained from Lyapunov function is adopted to compensate the influence of approximation error and system uncertainties on the control precision, which does not require priori knowledge of the bounds of the uncertainties and can avoid the gain-overestimation phenomenon. Finally, the stability analysis shows that the real sliding mode can be established in finite time. Simulation results and hardware-in-the-loop (HIL) experiments further demonstrate the effectiveness and superiority of the proposed control strategy.

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  • 收稿日期:2019-11-01
  • 最后修改日期:2021-02-21
  • 录用日期:2020-04-03
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