Safety-Certified Multi-Target Circumnavigation With Autonomous Surface Vehicles via Neurodynamics-Driven Distributed Optimization
Yue Jiang, Zhouhua Peng, Jun Wang
Abstract
This article addresses multitarget circumnavigation with autonomous surface vehicles (ASVs) subject to model nonlinearities, environmental disturbances, and physical constraints in the presence of stationary/moving obstacles. A neurodynamics-driven distributed optimization method is proposed to achieve safety-certified cooperative circumnavigation guided by multiple targets. Specifically, a cooperative circumnavigation guidance law based on a finite-time distributed observer is designed for surrounding multiple targets. Based on the geometric characteristics of multitarget circumnavigation, three collision-avoidance rules are formulated with respect to obstacles, targets, and ASVs; and three types of control barrier functions are derived to encode the coupled safety constraints into state constraints. A distributed command governor optimization problem is formulated to generate optimal commanded guidance signals within the globally coupled state constraints. To compute optimal commands in real time, multiple recurrent neural networks (RNNs) are employed to solve a distributed optimization problem. An event-triggered communication scheme is designed for the communication among RNNs with reduced communication burden. A predictor-based fuzzy control law is designed to track safe velocity commands. The closed-loop system is proven to be input-to-state stable. Simulation results are elaborated to demonstrate the effectiveness of the safety-certified control method for ASVs to circumnavigate multiple targets with guaranteed safety.