【SCI3区通讯作者周帆】Event-triggered-based decentralized optimal control of modular robot manipulators using RNN identifier

Release time:2022-12-12  Hits:

• Affiliation of Author(s):电气与电子工程学院
• Teaching and Research Group:自动化
• Journal:Journal of Intelligent & Robotic Systems
• Funded by:国家自然科学基金项目
• Key Words:Modular robot manipulators; Joint torque feedback technique; Neuro-dynamic programming; Event-triggered mechanism; Decentralized tracking control
• Abstract:In this paper, an event-triggered-based decentralized tracking control method is proposed for modular robot manipulators (MRMs) using a recurrent neural network (RNN) and neuro-dynamic programming (NDP). The joint torque feedback (JTF) technique is introduced to model the MRM subsystems. The cost function of each subsystem consists of a tracking error fusion function and a term summarizing the RNN identifier errors. The event-triggered Hamiltonian-Jacobi-Bellman (ETHJB) equation is solved by constructing a critic neural network using NDP, and a decentralized optimal tracking control policy under the event-triggered framework can be obtained. The closed-loop MRM system is shown to be uniformly ultimately bounded under the Lyapunov stability theorem. Finally, the experimental results verify that the proposed control method is superior to the time-triggered optimal control policy and the observer-critic based event-triggered optimal control policy proposed in the previous work of the author.
• Co-author:潘强,mabing,安天骄,zhoufan,周帆
• First Author:liyuanchun
• Indexed by:Journal paper
• Volume:106
• Issue:3
• Page Number:1
• ISSN No.:0921-0296
• Translation or Not:no
• Date of Publication:2022-10-20