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Appl. Sci. 2018, 8(1), 31; https://doi.org/10.3390/app8010031

Germinal Center Optimization Applied to Neural Inverse Optimal Control for an All-Terrain Tracked Robot

1
Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, 44430 Guadalajara, Mexico
2
Frankfurt Institute For Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
*
Author to whom correspondence should be addressed.
Received: 16 November 2017 / Revised: 15 December 2017 / Accepted: 21 December 2017 / Published: 27 December 2017
(This article belongs to the Special Issue Bio-Inspired Robotics)
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Abstract

Nowadays, there are several meta-heuristics algorithms which offer solutions for multi-variate optimization problems. These algorithms use a population of candidate solutions which explore the search space, where the leadership plays a big role in the exploration-exploitation equilibrium. In this work, we propose to use a Germinal Center Optimization algorithm (GCO) which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection. GCO is used to find an optimal set of parameters for a neural inverse optimal control applied to all-terrain tracked robot. In the Neural Inverse Optimal Control (NIOC) scheme, a neural identifier, based on Recurrent High Orden Neural Network (RHONN) trained with an extended kalman filter algorithm, is used to obtain a model of the system, then, a control law is design using such model with the inverse optimal control approach. The RHONN identifier is developed without knowledge of the plant model or its parameters, on the other hand, the inverse optimal control is designed for tracking velocity references. Applicability of the proposed scheme is illustrated using simulations results as well as real-time experimental results with an all-terrain tracked robot. View Full-Text
Keywords: Germinal Center Optimization; Artificial Immune Systems; Evolutionary Computing; neural identification; inverse optimal control; extended kalman filter Germinal Center Optimization; Artificial Immune Systems; Evolutionary Computing; neural identification; inverse optimal control; extended kalman filter
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Villaseñor, C.; Rios, J.D.; Arana-Daniel, N.; Alanis, A.Y.; Lopez-Franco, C.; Hernandez-Vargas, E.A. Germinal Center Optimization Applied to Neural Inverse Optimal Control for an All-Terrain Tracked Robot. Appl. Sci. 2018, 8, 31.

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