Discrete adaptive algorithms and neural network controllers for nonlinear dynamics

2005 ◽  
pp. 170-184
Author(s):  
M. B. Pszczel ◽  
T. Payne
Keyword(s):  
Neural Network ◽  
Nonlinear Dynamics ◽  
Adaptive Algorithms ◽  
Neural Network Controllers

scholarly journals $$\mathsf {SceneChecker}$$: Boosting Scenario Verification Using Symmetry Abstractions

2021 ◽  
pp. 580-594
Author(s):  
Hussein Sibai ◽  
Yangge Li ◽  
Sayan Mitra
Keyword(s):  
Neural Network ◽  
Nonlinear Dynamics ◽  
Hybrid System ◽  
Analysis Tool ◽  
Aerial Vehicles ◽  
Verification Problem ◽  
Verification Time ◽  
System Verification ◽  
Refinement Algorithm ◽  
Neural Network Controllers

AbstractWe present $$\mathsf {SceneChecker}$$ SceneChecker , a tool for verifying scenarios involving vehicles executing complex plans in large cluttered workspaces. $$\mathsf {SceneChecker}$$ SceneChecker converts the scenario verification problem to a standard hybrid system verification problem, and solves it effectively by exploiting structural properties in the plan and the vehicle dynamics. $$\mathsf {SceneChecker}$$ SceneChecker uses symmetry abstractions, a novel refinement algorithm, and importantly, is built to boost the performance of any existing reachability analysis tool as a plug-in subroutine. We evaluated $$\mathsf {SceneChecker}$$ SceneChecker on several scenarios involving ground and aerial vehicles with nonlinear dynamics and neural network controllers, employing different kinds of symmetries, using different reachability subroutines, and following plans with hundreds of waypoints in complex workspaces. Compared to two leading tools, DryVR and Flow*, $$\mathsf {SceneChecker}$$ SceneChecker shows 14$$\times $$ × average speedup in verification time, even while using those very tools as reachability subroutines.

scholarly journals Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network

2021 ◽  
Vol 3 (4) ◽  
pp. 344-354 ◽  
Author(s):  
Lauri Salmela ◽  
Nikolaos Tsipinakis ◽  
Alessandro Foi ◽  
Cyril Billet ◽  
John M. Dudley ◽  
...  
Keyword(s):  
Neural Network ◽  
Nonlinear Dynamics ◽  
Recurrent Neural Network ◽  
Fibre Optics

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.

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