期刊
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
卷 26, 期 3, 页码 1083-1090出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2017.2699167
关键词
Autonomous surface vehicles (ASVs); bound-constrained quadratic programming; constant bearing (CB); distributed maneuvering; fuzzy systems; recurrent neural network (RNN)
资金
- National Natural Science Foundation of China [51579023, 61673330, 61673081]
- Hong Kong Scholars Program [XJ2015009]
- China Post-Doctoral Science Foundation [2015M570247]
- High Level Talent Innovation and Entrepreneurship Program of Dalian [2016RQ036]
- Research Grants Council of the Hong Kong Special Administrative Region, China [14207614]
- Fundamental Research Funds for the Central Universities [3132016313]
This brief is concerned with the distributed maneuvering of multiple autonomous surface vehicles guided by a virtual leader moving along a parameterized path. In the guidance loop, a distributed guidance law is developed by incorporating a constant bearing strategy into a path-maneuvering design such that a prescribed formation pattern can be reached. To optimize the guidance signal under velocity constraint as well as minimize control torque during transient phase, an optimization-based command governor is employed to generate an optimal guidance signal for vehicle kinetics. The optimization problem is formulated as a bound-constrained quadratic programming problem, which is solved using a recurrent neural network. In the control loop, an estimator is developed where a fuzzy system is used to approximate unknown kinetics based on input and output data. Next, a kinetic control law is constructed based on the optimal command signal and the fuzzy-system-based estimator. By virtue of cascade stability analysis, it is proven that distributed maneuvering errors converge to a residual set. The simulation results illustrate the efficacy of the proposed method.
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