PDE-based multi-agent formation control using flatness and backstepping: Analysis, design and robot experiments

Freudenthaler, Gerhard; Meurer, Thomas

doi:10.1016/j.automatica.2020.108897

Cited by 86 publications

(33 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Block diagram of the scheme performed for WMR (7) by ( ), ( ) m t n t respectively, then the tracking error and the input data can be represented as…”

Section: Figurementioning

confidence: 99%

“…In recent years, WMR has been widely used in the logistics industry, military reconnaissance, agricultural transportation and other practical projects [24], so the control of WMR has been rapidly developed, such as sliding mode control [27], backstepping control [7], adaptive control [15], feedback control [19] and observer-based control [1], etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Iterative Learning Formation control for Nonholonomic Multiple Wheeled Mobile Robots with Channel Noise

Bu¹,

Hou

Yin

et al. 2021

ITC

View full text Add to dashboard Cite

In this paper, we studied the robust formation control issue of multiple non-holonomic wheel mobile robots (WMRs) with nonlinear characteristics and considered the channel noise and switching communication topology, a distributed iterative learning formation control (DILFC) scheme using information interaction between robots is proposed. Firstly, the formation tracking error with consensus information is constructed, and the relationship between formation error and channel noise is obtained from the nonlinear system model of mobile robot. Next, the controller is designed based on the prediction and the current learning term between robots, and the switching topology is introduced into the formation algorithm in the form of piecewise function. The sufficient condition and norm upper bound for the formation tracking stability of the system are obtained by theoretical analysis. The results show that although the channel noise accumulates in both the time domain and iteration domain, the validity of formation tracking can be guaranteed by adjusting the sampling time of the system. To illustrate the effectiveness of the proposed scheme, numerical simulation results of a group of WMRs are presented.

show abstract

“…Block diagram of the scheme performed for WMR (7) by ( ), ( ) m t n t respectively, then the tracking error and the input data can be represented as…”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Iterative Learning Formation control for Nonholonomic Multiple Wheeled Mobile Robots with Channel Noise

Bu¹,

Hou

Yin

et al. 2021

ITC

View full text Add to dashboard Cite

show abstract

“…With the continuous development of wireless communication technology, multi-robot systems have attracted widespread attention in the field of intelligent control and have been used in scenarios such as resource exploration, forest fire monitoring, map construction, and disaster rescue. Multi-robot formation control [1][2][3], as an important branch of multi-intelligence systems, requires multiple robots to maintain a certain formation and avoid collisions while performing tasks, while minimizing the impact of environmental constraints on the multi-robots and influencing them to complete a specific task. Over the years, a large number of research results have been produced for the multi-robot formation control problem, and a variety of formation control methods represented by the follower method, the behavior-based control method, the virtual structure method, and the artificial potential field method have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

et al. 2022

View full text Add to dashboard Cite

In view of the low formation redundancy in the traditional rigid formation algorithm and its difficulty in dynamically adapting to the external environment, this study considers the use of the CVT (centroidal Voronoi tessellation) algorithm to control multiple robots to form the desired formation. This method significantly increases the complexity of the multi-robot system, its structural redundancy, and its internal carrying capacity. First, we used the CVT algorithm to complete the Voronoi division of the global map, and then changed the centroid position of the Voronoi cell by adjusting the density function. When the algorithm converged, it could ensure that the position of the generated point was the centroid of each Voronoi cell and control the robot to track the position of the generated point to form the desired formation. The use of traditional formations requires less consideration of the impact of the actual environment on the health of robots, the overall mission performance of the formation, and the future reliability. We propose a health optimization management algorithm based on minor changes to the original framework to minimize the health loss of robots and reduce the impact of environmental restrictions on formation sites, thereby improving the robustness of the formation system. Simulation and robot formation experiments proved that the CVT algorithm could control the robots to quickly generate formations, easily switch formations dynamically, and solve the formation maintenance problem in obstacle scenarios. Furthermore, the health optimization management algorithm could maximize the life of unhealthy robots, making the formation more robust when performing tasks in different scenarios.

show abstract

“…Background and motivation : Practical systems are always subject to certain operational constraints, due to, for instance, physical limitation, safety consideration and/or specific performance requirement, and so on, making the underlying control problem much more challenging as compared with the case without constraint, 1‐4 which has enticed increasing research attention from control community and resulted in a rich collection of control methods (e.g., References 5‐9 and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Unified tracking control under full‐state constraints imposed irregularly

Cui

Wang

Song

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

It is nontrivial to address the control problem of nonlinear systems with irregular constraining conditions (where the constraints, possibly time‐varying, asymmetric and alternating positively and negatively, would occur or disappear in the middle of system operation). In this work, we present a control design framework for uncertain pure‐feedback systems under the aforementioned constraining conditions. By making use of auxiliary constraining boundaries, we analytically extend the originally imposed constraints over the entire time interval of system operation, with which we introduce a useful state transformation with unique features. It is such transformation that allows for the development of the control scheme capable of uniformly addressing a variety of constraints formed dynamically and imposed irregularly. It is shown that, for the scenarios where the constraints are partially or fully imposed or removed at any time instant during system operation, one only needs to reset the corresponding extended constraining boundaries in the control scheme instead of altering its structure. Both theoretical analysis and numerical simulation authenticate the effectiveness of the proposed method.

show abstract

PDE-based multi-agent formation control using flatness and backstepping: Analysis, design and robot experiments

Cited by 86 publications

References 30 publications

Distributed Iterative Learning Formation control for Nonholonomic Multiple Wheeled Mobile Robots with Channel Noise

Distributed Iterative Learning Formation control for Nonholonomic Multiple Wheeled Mobile Robots with Channel Noise

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

Unified tracking control under full‐state constraints imposed irregularly

Contact Info

Product

Resources

About