scholarly journals Neural-Network-Based Distributed Formation Tracking Control of Marine Vessels With Heterogeneous Hydrodynamics

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 150141-150149
Author(s):  
Cheng Liu
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Formation Tracking ◽  
Marine Vessels

scholarly journals Formation Tracking for Nonaffine Nonlinear Multiagent Systems Using Neural Network Adaptive Control

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Tongjuan Zhao ◽  
Jiuhe Wang ◽  
Jianhua Zhang
Keyword(s):  
Neural Network ◽  
Multiagent Systems ◽  
Tracking Control ◽  
Feedback System ◽  
Nonlinear Functions ◽  
Adaptive Tracking Control ◽  
Formation Tracking ◽  
Adaptive Law ◽  
Consensus Tracking ◽  
Time Form

Adaptive tracking control for distributed multiagent systems in nonaffine form is considered in this paper. Each follower agent is modeled by a nonlinear pure-feedback system with nonaffine form, and a nonlinear system is unknown functions rather than constants. Radial basis function neural networks (NNs) are employed to approximate the unknown nonlinear functions, and weights of NNs are updated by adaptive law in finite-time form. Then, the adaptive finite NN approach and backstepping technology are combined to construct the consensus tracking control protocol. Numerical simulation is presented to demonstrate the efficacy of suggested control proposal.

scholarly journals Adaptive Learning Based Tracking Control of Marine Vessels with Prescribed Performance

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhao Xu ◽  
Shuzhi Sam Ge ◽  
Changhua Hu ◽  
Jinwen Hu
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Tracking Control ◽  
Tracking Error ◽  
Adaptive Tracking ◽  
Performance Constraints ◽  
Tracking Controller ◽  
Unknown Disturbances ◽  
The Stability ◽  
Marine Vessels

A novel adaptive tracking controller of fully actuated marine vessels is proposed with completely unknown dynamics and external disturbances. The model of dominant dynamic behaviors and unknown disturbances of the vessel are learned by a neural network in real time. The controller is designed and it unifies backstepping and adaptive neural network techniques with predefined tracking performance constraints on the tracking convergence rate and the transient and steady-state tracking error. The stability of the proposed adaptive tracking controller of the vessel is proven with a uniformly bounded tracking error. The proposed adaptive tracking controller is shown to be effective in the tracking control of marine vessels by simulations.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters

2018 ◽  
Vol 73 ◽  
pp. 208-226 ◽  
Author(s):  
Dandan Wang ◽  
Qun Zong ◽  
Bailing Tian ◽  
Shikai Shao ◽  
Xiuyun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Time ◽  
Tracking Control ◽  
Disturbance Observer ◽  
Formation Tracking

scholarly journals Formation Tracking Control and Obstacle Avoidance of Unicycle-Type Robots Guaranteeing Continuous Velocities

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4374
Author(s):  
Jose Bernardo Martinez ◽  
Hector M. Becerra ◽  
David Gomez-Gutierrez
Keyword(s):  
Obstacle Avoidance ◽  
Tracking Control ◽  
Global Coordinate System ◽  
Avoidance Task ◽  
Time Varying ◽  
Control Laws ◽  
Formation Tracking ◽  
Control Scheme ◽  
First Time ◽  
Hierarchical Scheme

In this paper, we addressed the problem of controlling the position of a group of unicycle-type robots to follow in formation a time-varying reference avoiding obstacles when needed. We propose a kinematic control scheme that, unlike existing methods, is able to simultaneously solve the both tasks involved in the problem, effectively combining control laws devoted to achieve formation tracking and obstacle avoidance. The main contributions of the paper are twofold: first, the advantages of the proposed approach are not all integrated in existing schemes, ours is fully distributed since the formulation is based on consensus including the leader as part of the formation, scalable for a large number of robots, generic to define a desired formation, and it does not require a global coordinate system or a map of the environment. Second, to the authors’ knowledge, it is the first time that a distributed formation tracking control is combined with obstacle avoidance to solve both tasks simultaneously using a hierarchical scheme, thus guaranteeing continuous robots velocities in spite of activation/deactivation of the obstacle avoidance task, and stability is proven even in the transition of tasks. The effectiveness of the approach is shown through simulations and experiments with real robots.

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