Safety-Critical Cooperative Target Enclosing Control of Autonomous Surface Vehicles Based on Finite-Time Fuzzy Predictors and Input-to-State Safe High Order Control Barrier Functions

• Published in: IEEE transactions on fuzzy systems Vol. 32; no. 3; pp. 1 - 15
• Main Authors: Jiang, Yue, Peng, Zhouhua, Liu, Lu, Wang, Dan, Zhang, Fumin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous surface vehicle; Barriers; Closed loops; Collision avoidance; control barrier function; Control methods; Control systems; Control theory; Cooperative control; cooperative target enclosing; Disturbances; Feedback control; fuzzy predictor; Kinetic theory; Kinetics; Maneuvering targets; Neural networks; Quadratic programming; Safety; Safety critical; safety-critical control; Sea surface; Surface vehicles; Uncertainty; Vehicle dynamics; Vehicles
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2023.3309706

This paper addresses cooperative target enclosing of under-actuated autonomous surface vehicles (ASVs) subject to obstacles. Each ASV suffers from input constraints, in addition to unknown kinetics induced by model nonlinearities, unknown input gains, and external disturbances. A safety-critical cooperative target enclosing control method is proposed for surrounding a maneuvering target vehicle. Specifically, a finite-time fuzzy predictor is presented to learn the unknown kinetics with the integral of historical vehicle data. By using a distributed target estimator to recover the target position, a nominal distributed target enclosing control law is developed to achieve a circumnavigation formation. To avoid collisions between ASVs and obstacles/team-members, input-to-state safe high order control barrier functions are firstly introduced for encoding safety constraints. Based on the safety constraints and input constraints, a quadratic programming problem is formulated, and an optimal safety-critical control law is obtained by using projection neural networks to track the optimal solution. The closed-loop control system is proven to be input-to-state stable via Lyapunov theory. Moreover, the multiple ASV system is proven to be input-to-state safe regardless of high-order relative degree. The salient contributions of the proposed approach lie in finite-time fuzzy learning and collision-free target enclosing control under disturbances. Simulation results validate the effectiveness of the proposed safety-critical model-free control method for cooperatively surrounding a maneuvering target.