A decentralized receding horizon optimal approach to formation control of networked mobile robots

IF 2 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Optimal Control Applications & Methods Pub Date : 2017-01-01 DOI:10.1002/oca.2333
Mohammad Hosseinzadeh Yamchi, R. Mahboobi Esfanjani
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引用次数: 0

Abstract

This paper presents a receding horizon optimal controller with guaranteed stability for multirobot formation, taking into account collision and obstacle avoidance. The proposed scheme is based on synchronous decentralized strategy wherein all the vehicles that are connected via a packed‐delaying network solve a finite horizon–constrained optimal control problem to obtain their own control action at each sampling instant. First, each robot is modeled by a single integrator dynamics; then, by defining a control law for each robot and considering the effect of communication delay, the closed‐loop dynamics is described as a delay differential equation with tunable parameters. Afterwards, a novel finite‐horizon optimal control setup is established to obtain these adjustable gains such that a desirable formation is achieved. The efficiency and applicability of the suggested scheme are demonstrated by simulation results.
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网络化移动机器人编队控制的分散后退地平线最优方法
针对多机器人编队问题,提出了一种考虑碰撞和避障的保证稳定的后退地平线最优控制器。该方案基于同步分散策略,其中所有通过打包延迟网络连接的车辆在每个采样时刻求解有限水平约束最优控制问题,以获得各自的控制动作。首先,每个机器人由单个积分器动力学建模;然后,通过定义每个机器人的控制律,并考虑通信延迟的影响,将闭环动力学描述为具有可调参数的延迟微分方程。然后,建立了一种新的有限水平最优控制设置,以获得这些可调增益,从而实现理想的地层。仿真结果验证了该方案的有效性和适用性。
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来源期刊
Optimal Control Applications & Methods
Optimal Control Applications & Methods 工程技术-应用数学
CiteScore
3.90
自引率
11.10%
发文量
108
审稿时长
3 months
期刊介绍: Optimal Control Applications & Methods provides a forum for papers on the full range of optimal and optimization based control theory and related control design methods. The aim is to encourage new developments in control theory and design methodologies that will lead to real advances in control applications. Papers are also encouraged on the development, comparison and testing of computational algorithms for solving optimal control and optimization problems. The scope also includes papers on optimal estimation and filtering methods which have control related applications. Finally, it will provide a focus for interesting optimal control design studies and report real applications experience covering problems in implementation and robustness.
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