Bearing‐only neural network adaptive formation control using negative gradient method

2021 ◽  
Author(s):  
Qin Wang ◽  
Sulong Li ◽  
Enci Wang ◽  
Yang Yi
Keyword(s):  
Neural Network ◽  
Gradient Method ◽  
Formation Control ◽  
Negative Gradient

scholarly journals Adaptive-Neural-Network-Based Shape Control for a Swarm of Robots

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xuejing Lan ◽  
Zhenghao Wu ◽  
Wenbiao Xu ◽  
Guiyun Liu
Keyword(s):  
Neural Network ◽  
Nonlinear Dynamics ◽  
Shape Control ◽  
Formation Control ◽  
Adaptive Neural Network ◽  
Multirobot System ◽  
Control Scheme ◽  
Narrow Space ◽  
The Stability ◽  
Unknown Nonlinear Dynamics

This paper considers the region-based formation control for a swarm of robots with unknown nonlinear dynamics and disturbances. An adaptive neural network is designed to approximate the unknown nonlinear dynamics, and the desired formation shape is achieved by designing appropriate potential functions. Moreover, the collision avoidance, velocity consensus, and region tracking are all considered in the controller. The stability of the multirobot system has been demonstrated based on the Lyapunov theorem. Finally, three numerical simulations show the effectiveness of the proposed formation control scheme to deal with the narrow space, loss of robots, and formation merging problems.

scholarly journals Batch Gradient Method for Training of Pi-Sigma Neural Network with Penalty

2016 ◽  
Vol 7 (1) ◽  
pp. 11-20
Author(s):  
Kh. Sh Mohamed ◽  
Yan Liu ◽  
Wei Wu ◽  
Habtamu Z.A
Keyword(s):  
Neural Network ◽  
Gradient Method

scholarly journals Inverse design of grating couplers using the policy gradient method from reinforcement learning

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sean Hooten ◽  
Raymond G. Beausoleil ◽  
Thomas Van Vaerenbergh
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Method ◽  
Photonic Devices ◽  
Inverse Design ◽  
Grating Couplers ◽  
Electromagnetic Devices ◽  
Policy Gradient ◽  
Gradient Based ◽  
Local Gradient

Abstract We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic Optimization using REINFORCE Criteria for Enhanced Design). This technique uses a probabilistic generative neural network interfaced with an electromagnetic solver to assist in the design of photonic devices, such as grating couplers. We show that PHORCED obtains better performing grating coupler designs than local gradient-based inverse design via the adjoint method, while potentially providing faster convergence over competing state-of-the-art generative methods. As a further example of the benefits of this method, we implement transfer learning with PHORCED, demonstrating that a neural network trained to optimize 8° grating couplers can then be re-trained on grating couplers with alternate scattering angles while requiring >10× fewer simulations than control cases.

Convergence of gradient method for training ridge polynomial neural network

2012 ◽  
Vol 22 (S1) ◽  
pp. 333-339 ◽  
Author(s):  
Xin Yu ◽  
Fei Deng
Keyword(s):  
Neural Network ◽  
Gradient Method ◽  
Polynomial Neural Network

scholarly journals Distributed adaptive three-dimension formation control based on improved RBF neural network for non-linear multi-agent time-delay systems

2019 ◽  
Vol 13 (17) ◽  
pp. 2758-2765 ◽  
Author(s):  
Mingyang Zhang ◽  
Xinyi Yu ◽  
Peixuan Ding ◽  
Linlin Ou ◽  
Weidong Zhang
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Formation Control ◽  
Rbf Neural Network ◽  
Delay Systems ◽  
Three Dimension ◽  
Time Delay Systems ◽  
Non Linear ◽  
Multi Agent
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