TY - JOUR
T1 - Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation
AU - Peng, Zhouhua
AU - Wang, Jun
AU - Wang, Dan
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - Autonomous surface vehicles (ASVs)
KW - boundconstrained quadratic programming
KW - constant bearing (CB)
KW - distributed maneuvering
KW - fuzzy systems
KW - recurrent neural network (RNN)
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U2 - 10.1109/TCST.2017.2699167
DO - 10.1109/TCST.2017.2699167
M3 - RGC 21 - Publication in refereed journal
VL - 26
SP - 1083
EP - 1090
JO - IEEE Transactions on Control Systems Technology
JF - IEEE Transactions on Control Systems Technology
SN - 1063-6536
IS - 3
ER -