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
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 coo...
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| Published in | IEEE transactions on fuzzy systems Vol. 32; no. 3; pp. 1 - 15 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1063-6706 1941-0034 |
| DOI | 10.1109/TFUZZ.2023.3309706 |
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| Abstract | 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. |
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| AbstractList | 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. This article addresses cooperative target enclosing of underactuated 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 first 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 systems are 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. |
| Author | Jiang, Yue Peng, Zhouhua Zhang, Fumin Wang, Dan Liu, Lu |
| Author_xml | – sequence: 1 givenname: Yue orcidid: 0000-0001-8047-2961 surname: Jiang fullname: Jiang, Yue organization: School of Marine Electrical Engineering, Dalian Maritime University, Dalian, China – sequence: 2 givenname: Zhouhua orcidid: 0000-0003-4468-7281 surname: Peng fullname: Peng, Zhouhua organization: School of Marine Electrical Engineering, Dalian Maritime University, Dalian, China – sequence: 3 givenname: Lu surname: Liu fullname: Liu, Lu organization: School of Marine Electrical Engineering, Dalian Maritime University, Dalian, China – sequence: 4 givenname: Dan orcidid: 0000-0002-4099-6004 surname: Wang fullname: Wang, Dan organization: School of Marine Electrical Engineering, Dalian Maritime University, Dalian, China – sequence: 5 givenname: Fumin orcidid: 0000-0003-0053-4224 surname: Zhang fullname: Zhang, Fumin organization: Chen Kar-Shun Robotics Institute, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China |
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| Snippet | This paper addresses cooperative target enclosing of under-actuated autonomous surface vehicles (ASVs) subject to obstacles. Each ASV suffers from input... This article addresses cooperative target enclosing of underactuated autonomous surface vehicles (ASVs) subject to obstacles. Each ASV suffers from input... |
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| SubjectTerms | 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 |
| Title | 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 |
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