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An evolutionary intelligent control system for a flexible joints robot

Alejandro Pena, Juan C. Tejada Orcid Logo, Juan David Gonzalez-Ruiz Orcid Logo, Lina María Sepúlveda-Cano Orcid Logo, Francisco Chiclana, Fabio Caraffini Orcid Logo, Mario Gongora

Applied Soft Computing, Volume: 135, Start page: 110043

Swansea University Author: Fabio Caraffini Orcid Logo

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Abstract

In this paper, we present a model for a serial robotic system with flexible joints (RFJ) using Euler–Lagrange equations, which integrates the oscillatory dynamics generated by the flexible joints at specific operating points, using a pseudo-Ornstein-Uhlembeck process with reversion to the mean. We a...

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Published in: Applied Soft Computing
ISSN: 1568-4946
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa62420
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Abstract: In this paper, we present a model for a serial robotic system with flexible joints (RFJ) using Euler–Lagrange equations, which integrates the oscillatory dynamics generated by the flexible joints at specific operating points, using a pseudo-Ornstein-Uhlembeck process with reversion to the mean. We also propose a Stochastic Flexible - Adaptive Neural Integrated System (SF-ANFIS) to identify and control the RFJ with two degrees of freedom. For the configuration of the model, we use two adaptive strategies. One strategy is based on the Generalised Delta Rule (GDR). In contrast, a second strategy is based on the EDA-MAGO algorithm (Estimation Distribution Algorithms - Multi-dynamics Algorithm for Global Optimisation), improving online learning. We considered three stages for analysing and validating the proposed SF-ANFIS model: a first identification stage, a second stage defined by the adaptive control process, and a final stage or cancellation of oscillations. Results show that, for the identification stage, the SF-ANFIS model showed better statistical indices than the MADALINE model in control for the second joint, which presents the greatest oscillations; among those that stand out, the IOA (0.9955), VG (1.0012) and UAPC2 (-0.0003). For the control stage, The SF-ANFIS model showed, in a general way, the best behaviour in the system’s control for both joints, thanks to the capacity to identify and cancel oscillations based on the advanced sampling that defines the EDA algorithm. For the cancellation of the oscillations stage, the SF-ANFIS achieved the best behaviour, followed by the MADALINE model, where it is highlighted the UAPC2 (0.9525) value.
Keywords: Adaptive Neural Fuzzy Integrated Systems (ANFIS); Stochastic model; System control; Robotics; Ornstein–Uhlenbeck (OU)
College: Faculty of Science and Engineering
Start Page: 110043