Abstract
In this work, a leader-follower tracking and formation control strategy for mobile robots (MRs) with uncertain dynamics is proposed. This strategy utilizes a continual lifelong safe reinforcement learning (CLSRL) framework based on multilayer neural networks (MNNs). The proposed design employs actor-critic MNNs, incorporating a barrier function. This function is derived from the Bellman optimality principle. It addresses the state constraints throughout the control design process. A novel online continual lifelong learning (CLL) method is introduced for MR formation. This method leverages the Bellman residual error for weight significance in MNNs. It addresses catastrophic forgetting and interlayer dependence through layer-specific regularizers. Novel weight update laws are proposed. The simulation results show a 35% improvement in performance.
Recommended Citation
I. Ganie and S. Jagannathan, "Online Continual Safe Reinforcement Learning-Based Optimal Control of Mobile Robot Formations," 2024 IEEE Conference on Control Technology and Applications, CCTA 2024, pp. 519 - 524, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/CCTA60707.2024.10666606
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Formation Control; Mobile Robot; Neural Networks; Optimal Control
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
Comments
Office of Naval Research, Grant N00014-21-1-2232