Practical Formation Control for Multiple Anonymous Robots System with Unknown Nonlinear Disturbances

This paper mainly investigates formation control problems for a group of anonymous mobile robots with unknown nonlinear disturbances on a plane, in which all robots can asymptotically converge to any formation patterns without collision, and maintain any required relative distance with neighboring robots. To solve this problem, all robots are modeled as kinematic points and can only acquire information from other robots and their targets. Furthermore, a flexible distributed control law is designed to solve the formation problem while no collisions between any robots can be guaranteed during the whole process. The outstanding feature of the proposed control method is that it can force all mobile robots to form not only uniform circle formations but also non-uniform and non-circular formations with moving target centers. At last, both theoretical analysis and numerical simulations show the feasibility of the proposed control law.

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Finite-Time Switching Control of Nonholonomic Mobile Robots for Moving Target Tracking Based on Polar Coordinates

Complexity ◽

10.1155/2018/7360643 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 7

Author(s):

Hua Chen ◽

Shen Xu ◽

Lulu Chu ◽

Fei Tong ◽

Lei Chen

Keyword(s):

Mobile Robots ◽

Finite Time ◽

Control Method ◽

Tracking Error ◽

Switching Control ◽

Polar Coordinates ◽

Control Law ◽

Moving Target ◽

Nonholonomic Mobile Robots ◽

Time Switching

In this paper, finite-time tracking problem of nonholonomic mobile robots for a moving target is considered. First of all, polar coordinates are used to characterize the distance and azimuth between the moving target and the robot. Then, based on the distance and azimuth transported from the sensor installed on the robot, a finite-time tracking control law is designed for the nonholonomic mobile robot by the switching control method. Rigorous proof shows that the tracking error converges to zero in a finite time. Numerical simulation demonstrates the effectiveness of the proposed control method.

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DISTRIBUTED FORMATION CONTROL AND OBSTACLE AVOIDANCE OF MULTI-ROBOT SYSTEM

Journal of Science and Technology - IUH ◽

10.46242/jst-iuh.v40i04.516 ◽

2020 ◽

Vol 40 (04) ◽

Author(s):

HÀ TRỌNG NGHĨA ◽

TRẦN THANH KẾT ◽

NGUYỄN TẤN LUỸ

Keyword(s):

Mobile Robots ◽

Limit Cycle ◽

Distributed Control ◽

Trajectory Tracking ◽

Formation Control ◽

Control Algorithm ◽

Control Method ◽

Reference Trajectory ◽

Robot System ◽

Distributed Control Algorithm

This paper proposes a distributed control method for multi-mobile robots to avoid obstacles. Firstly, the Limit Cycle (LC) method is exploited to set the reference trajectory for robots to avoid obstacles. Secondly, the control rule that control a leading robot following the reference path is introduced. Thirdly, the algorithm that controls robots moving in a formation and avoiding obstacles based on the combination of the LC method and the reference trajectory tracking algorithm. Different from the distributed control algorithm in related documents, the algorithm in this paper ensures that the robot formation is not only maintained but also avoids obstacles when moving to the target. Finally, simulation and experimental results are conducted to verify the effectiveness of the proposed method.

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Adaptive 3D Distance-Based Formation Control of Multiagent Systems with Unknown Leader Velocity and Coplanar Initial Positions

Complexity ◽

10.1155/2018/1814653 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Xuejing Lan ◽

Wenbiao Xu ◽

Yun-Shan Wei

Keyword(s):

Theoretical Analysis ◽

Multiagent Systems ◽

Relative Position ◽

Formation Control ◽

Control Law ◽

Asymptotically Stable ◽

Coordinate Systems ◽

Lyapunov Theorem ◽

Globally Asymptotically Stable ◽

3 Dimensional

This paper considers the distributed 3-dimensional (3D) distance-based formation control of multiagent systems, where the agents are connected based on an acyclic minimally structural persistent (AMSP) graph. A parameter is designed according to the desired formation shape and is used to solve the problem that there are two formation shapes satisfying the same distance requirements. The unknown moving velocity of the leader agent is estimated adaptively by the followers requiring only the relative position measurements with respect to their local coordinate systems. In addition, the proposed formation controller provides a new way for the agent to leave the initial coplanar location. The 3D formation control law is globally asymptotically stable and has been demonstrated based on the Lyapunov theorem. Finally, two numerical simulations are presented to support the theoretical analysis.

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Lyapunov vector-based formation tracking control for unmanned aerial vehicles with obstacle/collision avoidance

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219879338 ◽

2019 ◽

Vol 42 (5) ◽

pp. 942-950

Author(s):

Kai Chang ◽

Dailiang Ma ◽

Xingbin Han ◽

Ning Liu ◽

Pengpeng Zhao

Keyword(s):

Unmanned Aerial Vehicles ◽

Potential Field ◽

Formation Control ◽

Control Method ◽

Spherical Surface ◽

Moving Target ◽

Local Optima ◽

Formation Tracking ◽

Aerial Vehicles ◽

Repulsive Forces

This paper presents a formation control method to solve the moving target tracking problem for a swarm of unmanned aerial vehicles (UAVs). The formation is achieved by the artificial potential field with both attractive and repulsive forces, and each UAV in the swarm will be driven into a leader-centered spherical surface. The leader is controlled by the attractive force by the moving target, while the Lyapunov vectors drive the leader UAV to a fly-around circle of the target. Furthermore, the rotational vector-based potential field is applied to achieve the obstacle avoidance of UAVs with smooth trajectories and avoid the local optima problem. The efficiency of the developed control scheme is verified by numerical simulations in four scenarios.

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Flocking Control of Mobile Robots with Obstacle Avoidance Based on Simulated Annealing Algorithm

Mathematical Problems in Engineering ◽

10.1155/2020/7357464 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Jin Cheng ◽

Bin Wang

Keyword(s):

Simulated Annealing ◽

Mobile Robots ◽

Motion Control ◽

Simulated Annealing Algorithm ◽

Control Method ◽

Sampling Period ◽

Potential Functions ◽

Control Law ◽

Optimal Position ◽

Annealing Algorithm

Flocking control problem of mobile robots under environment with unknown obstacles is addressed in this paper. Based on the simulated annealing algorithm, a flocking behaviour for mobile robots is achieved which converges to alignment while avoiding obstacles. Potential functions are designed to evaluate the positional relationship between robots and obstacles. Unlike the existing analytical method, simulated annealing algorithm is utilized to search the quasi-optimal position of robots in order to reduce the potential functions. Motion control law is designed to drive the robot move to the desired position at each sampling period. Experiments are implemented, and the results illustrate the effectiveness of the proposed flocking control method.

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Biologically-Inspired Learning and Adaptation of Self-Evolving Control for Networked Mobile Robots

Applied Sciences ◽

10.3390/app9051034 ◽

2019 ◽

Vol 9 (5) ◽

pp. 1034 ◽

Cited By ~ 5

Author(s):

Sendren Sheng-Dong Xu ◽

Hsu-Chih Huang ◽

Tai-Chun Chiu ◽

Shao-Kang Lin

Keyword(s):

Mobile Robots ◽

Formation Control ◽

Kinematic Model ◽

Control Law ◽

Biologically Inspired ◽

Modified Genetic Algorithm ◽

Robot Systems ◽

Learning And Adaptation ◽

Self Learning ◽

Adaptation Method

This paper presents a biologically-inspired learning and adaptation method for self-evolving control of networked mobile robots. A Kalman filter (KF) algorithm is employed to develop a self-learning RBFNN (Radial Basis Function Neural Network), called the KF-RBFNN. The structure of the KF-RBFNN is optimally initialized by means of a modified genetic algorithm (GA) in which a Lévy flight strategy is applied. By using the derived mathematical kinematic model of the mobile robots, the proposed GA-KF-RBFNN is utilized to design a self-evolving motion control law. The control parameters of the mobile robots are self-learned and adapted via the proposed GA-KF-RBFNN. This approach is extended to address the formation control problem of networked mobile robots by using a broadcast leader-follower control strategy. The proposed pragmatic approach circumvents the communication delay problem found in traditional networked mobile robot systems where consensus graph theory and directed topology are applied. The simulation results and numerical analysis are provided to demonstrate the merits and effectiveness of the developed GA-KF-RBFNN to achieve self-evolving formation control of networked mobile robots.

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Distributed formation control of networked mobile robots in environments with obstacles

Robotica ◽

10.1017/s0263574714002380 ◽

2014 ◽

Vol 34 (6) ◽

pp. 1403-1415 ◽

Cited By ~ 7

Author(s):

Whye Leon Seng ◽

Jan Carlo Barca ◽

Y. Ahmet Şekercioğlu

Keyword(s):

Mobile Robots ◽

Obstacle Avoidance ◽

Empirical Analysis ◽

Distributed Control ◽

Formation Control ◽

Control Mechanism ◽

Robot System ◽

Nonholonomic Robots ◽

Two Stages ◽

Robot Communication

SUMMARYA distributed control mechanism for ground moving nonholonomic robots is proposed. It enables a group of mobile robots to autonomously manage formation shapes while navigating through environments with obstacles. The mechanism consists of two stages, with the first being formation control that allows basic formation shapes to be maintained without the need of any inter-robot communication. It is followed by obstacle avoidance, which is designed with maintaining the formation in mind. Every robot is capable of performing basic obstacle avoidance by itself. However, to ensure that the formation shape is maintained, formation scaling is implemented. If the formation fails to hold its shape when navigating through environments with obstacles, formation morphing has been incorporated to preserve the interconnectivity of the robots, thus reducing the possibility of losing robots from the formation.The algorithm has been implemented on a nonholonomic multi-robot system for empirical analysis. Experimental results demonstrate formations completing an obstacle course within 12 s with zero collisions. Furthermore, the system is capable of withstanding up to 25% sensor noise.

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System Control In Fault And Normal Conditions Case Study - Quanser SRV-02

ACTA Universitatis Cibiniensis ◽

10.1515/aucts-2015-0078 ◽

2015 ◽

Vol 67 (1) ◽

pp. 133-138

Author(s):

Ionut Cristian Resceanu ◽

Cristina Floriana Resceanu

Keyword(s):

Fault Tolerance ◽

Theoretical Analysis ◽

Control Method ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Control Law ◽

System Control ◽

Sensor Fault ◽

Simulation Results

Abstract A fault tolerant control method is proposed for Quanser SRV-02 System in order to maintain the required performance in the presence of sensor failures. The proposed approach integrates control law and a sensor fault tolerance schema. Theoretical analysis and simulation results have confirmed the effectiveness of the proposed method.

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Broad-RBFNN-Based Intelligence Adaptive Antidisturbance Formation Control for a Class of Cluster Aerospace Unmanned Systems with Multiple High-Dynamic Uncertainties

Complexity ◽

10.1155/2021/6634175 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Erxin Gao ◽

Xin Ning ◽

Zheng Wang ◽

Xiaokui Yue

Keyword(s):

Disturbance Observer ◽

Formation Control ◽

Control Method ◽

Second Order ◽

Robust Adaptive Control ◽

Formation Flight ◽

Control Law ◽

Unmanned Systems ◽

High Dynamic ◽

Dynamic Uncertainties

This paper investigates the antidisturbance formation control problem for a class of cluster aerospace unmanned systems (CAUSs) suffering from multisource high-dynamic uncertainties. Firstly, to estimate and compensate the uncertainties existing in CAUS coordinate dynamics, an adaptive antidisturbance formation control law, which is combined by a robust adaptive control law and the second order disturbance observer, has been designed. Secondly, aiming at the adverse influences caused by the nonlinear time-varying nonlinearities existing in the formation flight dynamics, the radial basis function neural network (RBFNN) is introduced. Furthermore, considering the rapidly varying characteristics of the aforementioned formation flight nonlinearities, a novel board RBFNN (B-RBFNN) has been constructed and utilized to improve the approximation and compensation performance. In virtue of the fusing of the B-RBFNN and the second-order disturbance observer-based adaptive formation control law, the rapid response rate and the higher control accuracy of the formation control system can be achieved. As a result, a novel B-RBFNN-based intelligence adaptive antidisturbance formation control algorithm has been established for CAUS trajectory coordination and formation flight. Numerical simulation results are proposed to illustrate the effectiveness and advantages of the proposed B-RBFNN-based intelligent adaptive formation control method for the CAUS.

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SDRE based Leader-Follower Formation Control of Multiple Mobile Robots

TEMA (São Carlos) ◽

10.5540/tema.2014.015.02.0195 ◽

2014 ◽

Vol 15 (2) ◽

Author(s):

Caio Igor Gonçalves Chinelato ◽

L.S. Martins-Filho

Keyword(s):

Mobile Robots ◽

Formation Control ◽

Control Method ◽

Nonlinear Dynamical System ◽

Control Performance ◽

Font Size ◽

Multiple Mobile Robots ◽

Nonlinear Dynamical ◽

Control Techniques ◽

State Dependent

Formation control of multiple mobile robots is relatively a new area of robotics and increase the control performance and advantages of multiple mobile robots systems. <a name="OLE_LINK72">In this work we present a study concerning the modeling and formation control of a robotic system composed by two mobile robots, where one robot is the leader and the other is follower</a>. The system is a nonlinear dynamical system and cannot be controlled by traditional linear control techniques. The control strategy proposed is the SDRE (State-Dependent Riccati Equation) method. Simulations results with the software Matlab show the efficiency of the control method.

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