Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 31, Issue 3, Pages 972-983Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2912082
Keywords
Adaptive neural control; large-scale systems; stochastic disturbances
Categories
Funding
- National Natural Science Foundation of China [61773072, 61773051, 61673242, 61761166011]
- Innovative Talents Project of Liaoning Province of China [LR2016040]
- Natural Science Foundation of Liaoning Province of China [20180550691]
- Taishan Scholar Project of Shandong Province of China [2015162, tsqn201812093, ts201712040]
Ask authors/readers for more resources
This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed. In the control design process, NNs are applied to model the uncertain nonlinear functions, and adaptive control and backstepping are combined to construct the controller. The developed control scheme can guarantee that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded in fourth-moment. The simulation results demonstrate the effectiveness of the presented control scheme.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available