Formation Control of Multiple Mobile Robots Incorporating an Extended State Observer and Distributed Model Predictive Approach

This paper studies the extended state observer (ESO)-based distributed model predictive control (DMPC) approach to deal with multiple mobile robot formation with unknown disturbances. The distributed control problem with path parameters synchronization and disturbance rejection is formulated for for...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2020-11, Vol.50 (11), p.4587-4597
Main Authors: Liu, Andong, Zhang, Wen-An, Yu, Li, Yan, Huaicheng, Zhang, Rongchao
Format: Article
Language:English
Subjects:
Compensation
Control stability
Control systems design
Controllers
Distributed model predictive control (DMPC)
Dynamic models
extended state observer (ESO)
Feedback control
Feedforward control
Feedforward systems
formation control
Iterative algorithms
Iterative methods
Jamming
Mobile robots
multiple mobile robots
Observers
Optimization
Parameters
Performance indices
Pole placement
Predictive control
Robot control
Robustness
State observers
Synchronism
Synchronization
Tracking errors
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Summary:This paper studies the extended state observer (ESO)-based distributed model predictive control (DMPC) approach to deal with multiple mobile robot formation with unknown disturbances. The distributed control problem with path parameters synchronization and disturbance rejection is formulated for formation system according to the tracking error dynamic model, where the reference paths are parameterized. A local distributed controller is designed by using DMPC strategy for each mobile robot in the absence of disturbance by including parameter synchronization constraints in the quadratic performance index as coupling terms. The DMPC optimization problem is solved by using Nash-optimization iteration strategy with the maximum number of iteration constraint. To improve the ability of anti-jamming, a feedforward compensation controller is designed by using ESO method, where the ESO is designed by pole assignment. The convergence of the proposed iterative algorithm is given. Furthermore, the input-to-state stability property of the proposed composite controller, combining a feedforward compensation controller and local distributed controller, is analyzed for the closed-loop system. Finally, the validity of the proposed algorithm is verified by two simulation examples.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2018.2855444