Instantaneous Center of Rotation-Based Master-Slave Kinematic Modeling and Control

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS

Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-9123

V. Ramanathan, A. Zelenak, M. Pryor

引用次数: 0

Abstract

This article presents a novel kinematic model and controller design for a mobile robot with four Centered Orientable Conventional (COC) wheels. When compared to non-conventional wheels, COC wheels perform better over rough terrain, are not subject to vertical chatter and offer better braking capability. However, COC wheels are pseudo-omnidirectional and subject to nonholonomic constraints. Several established modeling and control techniques define and control the Instantaneous Center of Rotation (ICR); however, this method involves singular configurations that are not trivial to eliminate. The proposed method uses a novel ICR-based kinematic model to avoid these singularities, and an ICR-based nonlinear controller for one ‘master’ wheel. The other ‘slave’ wheels simply track the resulting kinematic relationships between the ‘master’ wheel and the ICR. Thus, the nonlinear control problem is reduced from 12th to 3rd-order, becoming much more tractable. Simulations with a feedback linearization controller verify the approach.

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基于瞬时旋转中心的主从运动建模与控制

本文提出了一种具有四个中心可定向常规(COC)轮的移动机器人的运动学模型和控制器设计。与非传统车轮相比，COC车轮在崎岖地形上表现更好，不受垂直颤振的影响，并提供更好的制动能力。然而，COC车轮是伪全向的，受非完整约束。几种已建立的建模和控制技术定义和控制瞬时旋转中心(ICR);然而，这种方法涉及到奇异的配置，这些配置是不容易消除的。该方法采用一种新颖的基于icr的运动模型来避免这些奇异性，并对一个“主”轮采用基于icr的非线性控制器。其他“从”轮简单地跟踪“主”轮和ICR之间产生的运动关系。因此，非线性控制问题从12阶降至3阶，变得更加易于处理。用反馈线性化控制器的仿真验证了该方法。

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.