Name
Title | ||
|---|---|---|
Applying neural fields to the stability problem of an inverted pendulum as a simple biped walking model |
Abstract | ||
|---|---|---|
This paper proposes a control architecture based on neural fields for a relatively complex and unstable dynamical system. The neural field model is capable of addressing goal-based planning problems and has properties, like embedding in an Euclidean space and linear stability, that potentially make it well-fitted for dynamic control tasks. The neural field control architecture is tested with the inverted pendulum problem. The cart-and-pole inverted pendulum is used as a simple biped walking model, where the cart models the center of pressure and the pole models the center of mass. The parameterized (i.e, non-evolved) neural field control architecture is compared against an evolved recurrent neural field controller applied to the same control task. The non-evolved neural field controller performs, in the simulation, better than the evolved recurrent neural network controller. Furthermore, the neural field has a spatial representation which allows an easy visualization of its field potentials. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IJCNN.2009.5179052 | IJCNN |
Keywords | Field | DocType |
stability problem,simple biped,non-evolved neural field controller,recurrent neural field controller,neural field,control task,dynamic control task,control architecture,inverted pendulum,field potential,neural field model,recurrent neural network controller,neural field control architecture,neural networks,center of mass,testing,stability analysis,euclidean space,process control,dynamic system,computer architecture,mathematical model,linear stability,center of pressure,computational intelligence,recurrent neural network,kernel,stability,motion planning,recurrent neural networks,acceleration | Motion planning,Kernel (linear algebra),Control theory,Inverted pendulum,Computational intelligence,Computer science,Control theory,Recurrent neural network,Artificial neural network,Dynamical system | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Figueredo | 1 | 0 | 0.34 |
| Jonatan Gómez | 2 | 241 | 29.70 |