scholarly journals Spiking Neural Controllers for Pushing Objects Around

2006 ◽  
pp. 570-581 ◽  
Author(s):  
Răzvan V. Florian
Keyword(s):  
Neural Controllers

scholarly journals Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

2021 ◽  
Vol 11 (6) ◽  
pp. 2535
Author(s):  
Bruno E. Silva ◽  
Ramiro S. Barbosa
Keyword(s):  
Neural Networks ◽  
Neural Control ◽  
Magnetic Levitation ◽  
Low Cost ◽  
Training Data ◽  
Neural Controllers ◽  
Levitation System ◽  
Internal Model Controller ◽  
Magnetic Levitation System ◽  
And Performance

In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.

Morphology dictates learnability in neural controllers

2020 ◽  
Author(s):  
Joshua Powers ◽  
Ryan Grindle ◽  
Sam Kriegman ◽  
Lapo Frati ◽  
Nick Cheney ◽  
...  
Keyword(s):  
Neural Controllers

Quantum genetic algorithm to evolve controllers for self-reconfigurable modular robots

2020 ◽  
Vol 17 (3) ◽  
pp. 427-435
Author(s):  
Mohamed Khalil Mezghiche ◽  
Noureddine Djedi
Keyword(s):  
Genetic Algorithms ◽  
Degrees Of Freedom ◽  
Optimization Problems ◽  
The Self ◽  
Modular Robots ◽  
Modular Robot ◽  
Adaptive Locomotion ◽  
Content Type ◽  
Quantum Genetic Algorithm ◽  
Neural Controllers

Purpose The purpose of this study is to explore using real-observation quantum genetic algorithms (RQGAs) to evolve neural controllers that are capable of controlling a self-reconfigurable modular robot in an adaptive locomotion task. Design/methodology/approach Quantum-inspired genetic algorithms (QGAs) have shown their superiority against conventional genetic algorithms in numerous challenging applications in recent years. The authors have experimented with several QGAs variants and real-observation QGA achieved the best results in solving numerical optimization problems. The modular robot used in this study is a hybrid simulated robot; each module has two degrees of freedom and four connecting faces. The modular robot also possesses self-reconfiguration and self-mobile capabilities. Findings The authors have conducted several experiments using different robot configurations ranging from a single module configuration to test the self-mobile property to several disconnected modules configuration to examine self-reconfiguration, as well as snake, quadruped and rolling track configurations. The results demonstrate that the robot was able to perform self-reconfiguration and produce stable gaits in all test scenarios. Originality/value The artificial neural controllers evolved using the real-observation QGA were able to control the self-reconfigurable modular robot in the adaptive locomotion task efficiently.

scholarly journals Clinical Relevance of State-of-the-Art Analysis of Surface Electromyography in Cerebral Palsy

2020 ◽  
Vol 11 ◽  
Author(s):  
Germana Cappellini ◽  
Francesca Sylos-Labini ◽  
Carla Assenza ◽  
Laura Libernini ◽  
Daniela Morelli ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Neurological Disorders ◽  
Surface Electromyography ◽  
Clinical Relevance ◽  
State Of The Art ◽  
Neuromuscular System ◽  
Neural Controllers ◽  
Children With Cerebral Palsy ◽  
Tendon Surgery ◽  
Reference Databases

Surface electromyography (sEMG) can be used to assess the integrity of the neuromuscular system and its impairment in neurological disorders. Here we will consider several issues related to the current clinical applications, difficulties and limited usage of sEMG for the assessment and rehabilitation of children with cerebral palsy. The uniqueness of this methodology is that it can determine hyperactivity or inactivity of selected muscles, which cannot be assessed by other methods. In addition, it can assist for intervention or muscle/tendon surgery acts, and it can evaluate integrated functioning of the nervous system based on multi-muscle sEMG recordings and assess motor pool activation. The latter aspect is especially important for understanding impairments of the mechanisms of neural controllers rather than malfunction of individual muscles. Although sEMG study is an important tool in both clinical research and neurorehabilitation, the results of a survey on the clinical relevance of sEMG in a typical department of pediatric rehabilitation highlighted its limited clinical usage. We believe that this is due to limited knowledge of the sEMG and its neuromuscular underpinnings by many physiotherapists, as a result of lack of emphasis on this important methodology in the courses taught in physical therapy schools. The lack of reference databases or benchmarking software for sEMG analysis may also contribute to the limited clinical usage. Despite the existence of educational and technical barriers to a widespread use of, sEMG does provide important tools for planning and assessment of rehabilitation treatments for children with cerebral palsy.

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