This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs)subject to model uncertainties and environmental disturbances.A safety-certified path-guided coordina...This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs)subject to model uncertainties and environmental disturbances.A safety-certified path-guided coordinated control method is proposed for multiple ASVs to achieve a distributed formation in obstacle environments.Specifically,a neural predictor with a high-order tuner is presented to approximate unknown nonlinearities with accelerated learning performance.Subsequently,control Lyapunov functions(CLFs)and control barrier functions(CBFs)are constructed for mapping stability constraints and safety constraints on states to control inputs.A quadratic optimization problem is constructed with the norm of control inputs as the objective function,CLFs and CBFs as constraints.Neurodynamic optimization is used to deal with the quadratic programming problem and generate the optimal kinetic control signals,thereby attaining the desired safe formation.Unlike the high-order CBF,a CBF backstepping method is proposed to establish safety constraints such that repeated time derivatives of system nonlinearities can be avoided.The multi-ASVs system is ensured to be input-to-state safe irrespective of high-order relative degree.Through the Lyapunov theory,the multi-ASVs system is proven to be input-to-state stable.Finally,simulation results are presented to validate the efficacy of the presented safety-certified distributed formation control for networked ASVs.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.52471372in part by the Key Basic Research of Dalian under Grant No.2023JJ11CG008+3 种基金in part by the Doctoral Scientific Research Foundation of Liaoning Province under Grant Nos.2024-BS-012 and 2023-BS-077in part by the Postdoctoral Research Foundation of China under Grant No.2024M751980in part by the Bolian Research Funds of Dalian Maritime University and the Fundamental Research Funds for the Central Universities under Grant Nos.3132024601,3132023508in part by the Open Project of State Key Laboratory of Maritime Technology and Safety under Grant No.SKLMTA-DMU2024Y3。
文摘This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs)subject to model uncertainties and environmental disturbances.A safety-certified path-guided coordinated control method is proposed for multiple ASVs to achieve a distributed formation in obstacle environments.Specifically,a neural predictor with a high-order tuner is presented to approximate unknown nonlinearities with accelerated learning performance.Subsequently,control Lyapunov functions(CLFs)and control barrier functions(CBFs)are constructed for mapping stability constraints and safety constraints on states to control inputs.A quadratic optimization problem is constructed with the norm of control inputs as the objective function,CLFs and CBFs as constraints.Neurodynamic optimization is used to deal with the quadratic programming problem and generate the optimal kinetic control signals,thereby attaining the desired safe formation.Unlike the high-order CBF,a CBF backstepping method is proposed to establish safety constraints such that repeated time derivatives of system nonlinearities can be avoided.The multi-ASVs system is ensured to be input-to-state safe irrespective of high-order relative degree.Through the Lyapunov theory,the multi-ASVs system is proven to be input-to-state stable.Finally,simulation results are presented to validate the efficacy of the presented safety-certified distributed formation control for networked ASVs.
