Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles With Uncertain Dynamics

By Zhouhua Peng, Dan Wang, Zhiyong Chen, Xiaojing Hu, Weiyao Lan. IEEE Transactions on Control Systems Technology, 2013

488 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/b3a2be702a6d36960d3442224e2f77b2c7cb0f06

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jun Wang
2. Zhouhua Peng
3. Dan Wang
4. Qing-Long Han
5. Xu Jin
6. Yangyang Chen
7. Yu-Ping Tian
8. Chenguang Yang

In Control and Systems Engineering

1. Jun Wang
2. Zhouhua Peng
3. Dan Wang
4. Qing-Long Han
5. Tie-shan Li
6. Xu Jin
7. Yu-Ping Tian
8. Rongxin Cui

In Ocean Engineering

1. Jun Wang
2. Zhouhua Peng
3. Dan Wang
4. Qing-Long Han
5. Xu Jin

Citations in the corpus

• Finite-Time Formation Control of Under-Actuated Ships Using Nonlinear Sliding Mode Control (2018, unknown): “For example, in [18], a dynamic surface control approach based on neural network is proposed.”
• Fault tolerant finite-time leader-follower formation control for autonomous surface vessels with LOS range and angle constraints (2016, methodology): “In this section we present a simulation study based on the model presented in Do and Pan (2006) and Peng et al. (2013), where the vessel dynamics are in the form of (1), with the kinetics Miν̇i + Ci(νi)νi + Di(νi)νi + Fi(νi) = τ Fi , (45) where i = 1, 2 meaning that there are two followers that we…”
• Output-Feedback Cooperative Formation Maneuvering of Autonomous Surface Vehicles With Connectivity Preservation and Collision Avoidance (2020, background): “First, in contrast to the state feedback formation control laws proposed in [13], [31]–[33], and [44] the output feedback formation maneuvering control laws without using velocity measurements are proposed herein.”
• Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation (2018, methodology): “Remark 1: Compared with the backstepping and DSC design methods in [1]–[4], [8]–[11], [21], and [31], the advantage of the RNN-based reference governor is twofold.”
• Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning (2017, methodology): “NN adaptive control was presented for multiple unmanned surface vessels in [43], and the unmeasured states were estimated by a local”
• Formation tracking and attitude synchronization control of underactuated ships along closed orbits (2015, background): “In [10, 11], a leader-following strategy is proposed to give a solution to the formation tracking control problem of multiple underactuated ships.”
• Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback (2017, background): “Compared with the multilayer NNs [38], [39], and feedforward NNs [40], ESNs have an effect of exponential smoothing.”
• An Overview of Recent Advances in Coordinated Control of Multiple Autonomous Surface Vehicles (2021, methodology): “single or multiple surface vehicles to track a single or multiple targets [11], [15], [29].”
• Output-Feedback Path-Following Control of Autonomous Underwater Vehicles Based on an Extended State Observer and Projection Neural Networks (2018, methodology): “In addition to handling vehicle uncertainty [3], [5], [6], [12], [18], [22], [36], [37], [43], it is necessary to adhere to strict”