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

• 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
• DOI: 10.1109/TNNLS.2022.3171828

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.