Distributed predictive formation control of networked mobile robots subject to communication delay

Yamchi, Mohammad Hosseinzadeh; Esfanjani, Reza Mahboobi

doi:10.1016/j.robot.2017.01.005

Cited by 53 publications

(24 citation statements)

References 32 publications

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“…Present dominant control strategies include that Dong et al 41 developed an approach adopting a switching interaction topologies to solve the time-varying formation control problem for UAVs. Yamchi et al 42 proposed a distributed predictive controller which helps improve system stability as well as avoid collisions en route for mobile robots. Li et al 43 improved the common receding horizon formation control to achieve stabilised tracking performance for AUVs.…”

Section: System Architecture Of Multi-vehicle Formationmentioning

confidence: 99%

A survey of formation control and motion planning of multiple unmanned vehicles

2018

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SUMMARYThe increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development.

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Section: System Architecture Of Multi-vehicle Formationmentioning

confidence: 99%

A survey of formation control and motion planning of multiple unmanned vehicles

2018

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“…Here, the proposed approach is applied in the case of satellite system where a vector measurement is provided by a magnetometer. In [23], a predictive strategy for maintaining the formation of mobile robots is proposed to solve the problem of communication delays. In [24], a nonlinear predictive observer is proposed to compensate for the delay introduced by a vision-based sensor and the application is trajectory tracking of nonholonomic WMRs.…”

Section: Introductionmentioning

confidence: 99%

Tracking Control for Wheeled Mobile Robot Based on Delayed Sensor Measurements

Guechi

Belharet

Blažič

2019

Sensors

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This paper proposes a novel Takagi-Sugeno fuzzy predictor observer to tackle the problem of the constant and known delay in the measurements. The proposed observer is developed for a trajectory-tracking problem of a wheeled mobile robot where a parallel-distributed compensation control is used to control the robot. The L2-stability of the proposed observer is also proven in the paper. Both, the control and the observer gains are obtained by solving the proposed system of linear matrix inequalities. To illustrate the efficiency of the proposed approach, an experimental comparison with another predictor observer was done.

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“…State‐of‐the‐art techniques for collision‐free of multi‐agent formation control mainly include the Lyapunov‐based method [13–15] and the optimisation‐based method [16–18]. In the artificial potential function (APF) approach, one of the Lyapunov‐based methods, the gradient of repulsive potential and an attractive potential are used for collision avoidance and the convergence to goals, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the APF method is not able to handle various constraints and consider optimality. Regarding the optimisation‐based approach with various constraints, the multi‐agent collision avoidance problem can be solved through distributed model predictive control (DMPC) [16–18], quadratic programming [19], mixed integer linear programming (MILP) [21], or sequential convex programming [22].…”

Section: Introductionmentioning

confidence: 99%

“…[17] also address the obstacle avoidance problem and Hosseinzadeh Yamchi and Mahboobi Esfanjani [18] further consider the communication delay among agents. The closed‐loop stability of the whole multi‐agent system in [16–18] is ensured by the development of the positively invariant terminal region and proper terminal controller. The intuition of this method is to let the agents get close enough to the desired goals such that tracking a controller can be applied without considering the collisions with agents and obstacles.…”

Section: Introductionmentioning

confidence: 99%

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