Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation

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.