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Backstepping Neurodynamics based Position-Tracking Control of Underactuated autonomous surface vehicles

Backstepping Neurodynamics based Position-Tracking Control o...

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第25届中国控制与决策会议

作者： Chang-Zhong Pan Xu-Zhi Lai Simon X. Yang Min Wu School of Information Science and Engineering Central South University Hunan Engineering Laboratory for Advanced Control and Intelligent Automation School of Engineering University of Guelph

ISBN: (纸本)9781467355339

Backstepping is a very effective methodology for tracking control of underactuated autonomous surface vehicles （ASVs）, but the fundamental drawback is that it requires to calculate numerical derivatives of virtual velocity control signals. In this paper, we propose a novel bioinspired neurodynamics based approach by integrating the backstepping technique and three shunting neural dynamics models. Instead of differentiating the virtual control signal directly at each step of the backstepping design procedure, we let the corresponding virtual signal pass through a shunting neuron model to avoid the complexity of differentiation. The proposed tracking control algorithm is thus much simpler than that constructed based on the conventional backstepping approach. The stability of the control system is proved that all signals are uniformly ultimately bounded （UUB）, and the position-tracking error converges to a small neighborhood of the origin. Simulations demonstrate the effectiveness of the proposed approach.

关键词： Neurodynamics Backstepping Tracking Control Underactuated Systems autonomous surface vehicles

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Robust Adaptive Formation Control with autonomous surface vehicles

Robust Adaptive Formation Control with Autonomous Surface Ve...

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第二十九届中国控制会议

作者： PENG Zhouhua1,WANG Dan1,YAO Yubin1,LAN Weiyao2,LI Xiaoqiang1,SUN Gang1 1.Marine Engineering College,Dalian Maritime University,Dalian 116026,P.R.China2.Department of Automation,Xiamen University,Xiamen 361005,P.R.China

In this paper,a robust adaptive formation control law is proposed for multiple autonomous surface vehicles moving in a leader-follower *** presented method uses only measurements of line-of-sight range and angle by local sensors,no other information about leader is required for control *** networks are trained online to compensate for uncertainties due to unknown velocity of the *** on Lyapunov synthesis,the proposed adaptive NN control ensures that both relative distance and angle track the given bounded reference signals to a small neighborhood of *** effectiveness of proposed formation controller with applications to three autonomous surface vehicles is illustrated by numerical simulations.

关键词： Formation Control Robust Adaptive Control Neural Networks autonomous surface vehicles

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