A Behavior-based Adaptive Dynamic Programming Method for Multiple Mobile Manipulators Coordination Control

In this work, a behavior-based adaptive dynamic programming (BADP) method is proposed for coordination control of unmanned ground vehicle-manipulator systems (UGVMs). Through a null-space-based behavioral control (NSBC) framework, the multi-objective coordination control is transformed into a single...

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Vydáno v:International journal of control, automation, and systems Ročník 21; číslo 9; s. 3022 - 3035
Hlavní autoři: Zhang, Zhenyi, Chen, Jianfei, Mo, Zhibin, Chen, Yutao, Huang, Jie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.09.2023
Springer Nature B.V
제어·로봇·시스템학회
Témata:
Adaptive control
Algorithms
Control
Control methods
Control systems design
Controllers
Coordination
Cost function
Design
Dynamic programming
Engineering
Kinematics
Machine learning
Manipulators
Mechatronics
Methods
Optimal control
Optimization
Pareto optimum
Regular Papers
Robot arms
Robotics
Tracking control
Unmanned ground vehicles
제어계측공학
behavioral control
Adaptive dynamic programming
UGVMs
multi-objective mission
ISSN:1598-6446, 2005-4092
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Popis
Shrnutí:In this work, a behavior-based adaptive dynamic programming (BADP) method is proposed for coordination control of unmanned ground vehicle-manipulator systems (UGVMs). Through a null-space-based behavioral control (NSBC) framework, the multi-objective coordination control is transformed into a single-objective tracking control at the mission layer. Since cost functions and control constraints are simplified at control layer, the complexity of controller design is reduced. Then, an identifier-actor-critic reinforcement learning algorithm framework is introduced to learn the optimal control policy by balancing the control performance and consumption. Simulation results show that control costs are reduced around 13.5% per sampling period compared to existing multiple objective control methods. Finally, the BADP method is experimentally validated using four real UGVMs.
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content type line 14
http://link.springer.com/article/10.1007/s12555-021-0904-4
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-021-0904-4