Structured Robust Linear Parameter-Varying Vehicle Sideslip Angle Estimation

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS

Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-9021

Jingqiang Zha, Junmin Wang, Min Li, Xin Zhang, Xiao Yu

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引用次数: 0

Abstract

Non-smooth structured robust controller design has drawn a lot of attention recently due to its ability to deal with uncertainty and its convenience for implementation. In this paper, the method is extended to design the structured robust linear parameter-varying (LPV) estimator by pulling out scheduling variables from estimator using linear fractional transformation (LFT). The structured robust LPV estimator is then applied to vehicle sideslip angle estimation. Both the measured vehicle speed and estimated tire cornering stiffness are treated as scheduling variables to further reduce sideslip angle estimation error. The effects of estimator order and number of repetitiveness of scheduling variables are studied using a MATLAB/Simulink bicycle model. The developed approach is later verified in Hardware-in-the-Loop (HIL) simulation environment using dSPACE SCALEXIO and MicroAutoBox. A comprehensive high-fidelity dSPACE automotive simulation models (ASM) vehicle model is used for the real-time HIL simulation. Double-lane change and sine steer maneuvers have been implemented to verify the effectiveness of the structured robust LPV sideslip angle estimation method.

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结构鲁棒线性变参数车辆侧滑角估计

非光滑结构鲁棒控制器设计由于其处理不确定性的能力和实现的便利性，近年来引起了人们的广泛关注。本文将该方法推广到利用线性分数变换(LFT)从估计量中提取调度变量来设计结构化鲁棒线性变参估计量。然后将结构鲁棒LPV估计器应用于车辆侧滑角估计。将测量的车速和估计的轮胎转弯刚度作为调度变量，进一步减小侧滑角估计误差。利用MATLAB/Simulink自行车模型研究了调度变量的估计器阶数和重复次数对调度的影响。开发的方法随后在硬件在环(HIL)仿真环境中使用dSPACE SCALEXIO和MicroAutoBox进行了验证。采用综合高保真度的dSPACE汽车仿真模型(ASM)车辆模型进行实时HIL仿真。通过双车道变道和正弦转向操作验证了结构鲁棒LPV侧滑角估计方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.40

自引率

66.70%

发文量

期刊介绍： This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.