Cooperative Deterministic Learning-Based Formation Control for a Group of Nonlinear Uncertain Mechanical Systems
Document Type
Article
Date of Original Version
1-1-2019
Abstract
This paper addresses the formation control problem for a group of mechanical systems with nonlinear uncertain dynamics under the virtual leader-following framework. New cooperative deterministic learning-based adaptive formation control algorithms are proposed. Specifically, the virtual leader dynamics is constructed as a linear system subject to unknown bounded inputs, so as to produce more diverse reference signals for formation tracking control. A cooperative discontinuous nonlinear estimation protocol is first proposed to estimate the leader's state information. Based on this, a cooperative deterministic learning formation control protocol is developed using artificial neural networks, such that formation tracking control and locally-accurate nonlinear identification with learning knowledge consensus can be achieved simultaneously. Finally, by utilizing the learned knowledge represented by constant neural networks, an experience-based distributed control protocol is further proposed to enable position-swappable formation control. Numerical simulations using a group of autonomous underwater vehicles have been conducted to demonstrate the effectiveness and usefulness of the proposed results.
Publication Title, e.g., Journal
IEEE Transactions on Industrial Informatics
Volume
15
Issue
1
Citation/Publisher Attribution
Yuan, Chengzhi, Haibo He, and Cong Wang. "Cooperative Deterministic Learning-Based Formation Control for a Group of Nonlinear Uncertain Mechanical Systems." IEEE Transactions on Industrial Informatics 15, 1 (2019): 319-333. doi: 10.1109/TII.2018.2792455.