A hybridized neuro-genetic solution for controlling industrial R³ workspace

Title:

A hybridized neuro-genetic solution for controlling industrial R³ workspace

Creator:

Irigoyen , E., Larrea, M., Valera, J., Gómez , V., and Artaza, F.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:6b48fa08-5cb8-45a8-8df3-1f3005f1aefb
uuid:6b48fa08-5cb8-45a8-8df3-1f3005f1aefb

Subject:

Hybrid neuro-genetic solution, optimal trajectory generation, multi-objective genetic algorithm, nonlinear neural control, and adaptive predictive control

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This work presents a hybridized neuro-genetic control solution for R³ workspace application. The solution is based on a multi-objective genetic algorithm reference generator and an adaptive predictive neural network strategy. The trajectory calculation between two points in an R3 workspace is a complex optimization problem considering the fact that there are multiple objectives, restrictions and constraint functions which can play an important role in the problem and be in competition. We solve this problem using genetic algorithms, in a multi objective optimization strategy. Subsequently, we enhance a training algorithm in order to achieve the best adaptation of the neural network parameters in the controller which is responsible for generating the control action for a nonlinear system. As an application of the proposed hybridized control scheme, a crane tracking control is presented.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:7

Harvested from:

CDK

Metadata only:

false