Cooperative Deterministic Learning-Based Formation Control for a Group of Nonlinear Uncertain Mechanical Systems
Date of Original Version
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.
IEEE Transactions on Industrial Informatics
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.