Adaptive Dynamic Programming-Based Cooperative Motion/Force Control for Modular Reconfigurable Manipulators: A Joint Task Assignment Approach

This article develops a cooperative motion/force control (CMFC) scheme based on adaptive dynamic programming (ADP) for modular reconfigurable manipulators (MRMs) with the joint task assignment approach. By separating terms depending on local variables only, the dynamic model of the entire MRM system...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 34; no. 12; pp. 10944 - 10954
Main Authors: Zhao, Bo, Zhang, Yongwei, Liu, Derong
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive control
Adaptive dynamic programming (ADP)
Closed loops
Cooperative control
Decentralized control
dynamic constraints
Dynamic models
Dynamic programming
Dynamics
End effectors
Force
Jacobi matrix method
Jacobian matrix
Joining
Manipulator dynamics
Manipulators
modular reconfigurable manipulators (MRMs)
Modules
motion/force control
Neural networks
neural networks (NNs)
Reconfiguration
Stability analysis
Task analysis
Tracking
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:This article develops a cooperative motion/force control (CMFC) scheme based on adaptive dynamic programming (ADP) for modular reconfigurable manipulators (MRMs) with the joint task assignment approach. By separating terms depending on local variables only, the dynamic model of the entire MRM system can be regarded as a set of joint modules interconnected by coupling torque. In addition, the Jacobian matrix, which reflects the interaction force of the MRM end-effector, can be mapped into each joint. Using this approach, both the motion and force tasks on the end-effector of the entire MRM system can be assigned to each joint module cooperatively. Then, by substituting the actual states of coupled joint modules with their desired ones, the norm-boundedness assumption on the interconnection of joint module can be relaxed. By using the measured input-output data of each joint module, a neural network (NN)-based robust decentralized observer, which guarantees the observation error to be asymptotically stable is established. An improved local value function is constructed for each joint module to reflect the interconnection. Then, the local Hamilton-Jacobi-Bellman equation is solved by constructing a local critic NN with a nested learning structure. Hereafter, the ADP-based CMFC is obtained by the assistance of force feedback compensation. Based on the Lyapunov stability analysis, the closed-loop MRM system is guaranteed to be uniformly ultimately bounded under the present ADP-based CMFC scheme. The simulation on a two-degree of freedom MRM system demonstrates the effectiveness of the present control approach.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3171828