脚轮车 - 带脚轮的欠驱动地面车辆

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS
S. Korczak
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引用次数: 0

摘要

本文介绍了一种名为 "脚轮车 "的新型欠驱动地面移动机器人。该平台由一个前驱动转向轮和两个不受控制的后脚轮组成。所介绍的脚轮车模型是移动机器人的一种有趣的替代方案,它兼具气垫船和传统四轮汽车的动态特性。脚轮车的欠驱动特性导致传统的比例-衍生反馈控制只能影响选定的配置变量。本文提出了三种脚轮车数学模型:带自由移动脚轮的动态模型、带阻塞脚轮的动态模型和简化气垫船描述。在使用选择性和全状态控制执行轨迹要求较高的跟踪任务时,对模型进行了测试。全状态控制是基于计算扭矩技术、伪逆运算和比例-衍生反馈。它能够抑制失控方向的不稳定行为,但代价是整体效果(位置误差增大)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Caster Car – underactuated ground vehicle with caster wheels
This paper presents a new type of underactuated ground mobile robot called Caster Car. The platform consists of a front-driven and steered wheel and two uncontrolled rear caster wheels. The Caster Car model presented can be an interesting alternative for mobile robots that connects dynamic properties of hovercrafts and classical 4-wheeled cars. Underactuated properties of the Caster Car cause that classical proportional-derivative feedback control give the ability to affect only selected configuration variables. Three mathematical models of the Caster Car are proposed: a dynamic model with free-moving casters, a dynamic model with blocked casters, and a simplified hovercraft description. Models were tested during tracking tasks with demanding trajectory using selective and full-state control. This full state control was based on the computed torque technique with the pseudoinverse operation and proportional-derivative feedback. It gives the ability to suppress unstable behaviors of uncontrolled orientation but in cost of overall effect (higher position errors).
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来源期刊
Archives of Control Sciences
Archives of Control Sciences Mathematics-Modeling and Simulation
CiteScore
2.40
自引率
33.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
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