An evolutionary intelligent control system for a flexible joints robot

Peña, Alejandro; Tejada, Juan C.; González-Ruiz, Juan David; Sepúlveda-Cano, Lina María; Chiclana, Francisco; Caraffini, Fabio; Góngora, Mario

doi:10.1016/j.asoc.2023.110043

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…Autonomous underwater vehicles are mostly powered by lithium batteries, and when equipped with apparatuses and devices, they can conduct continuous cruise and autonomous operations within a wide area. The advancements of numerical calculation [1], computer simulation [2], data mining [3,4], system identification [5], machine learning [6], intelligent control [7] and artificial intelligence technology [8] have brought significant breakthroughs to the domain of underwater vehicles. AUVs are becoming increasingly important in operations where equipment and divers have difficulties to complete, for example, dam detection [9], detection of wrecked ships or aircraft [10], undersea pipeline service [11], exploitation of submarine resources [12,13], submarine cable maintenance [14] and submarine three-dimensional terrain mapping [15], etc.…”

Section: Introductionmentioning

confidence: 99%

A LSSVR Interactive Network for AUV Motion Control

Jiang

Wan

Zhang

et al. 2023

JMSE

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In view of the requirements on control precision of autonomous underwater vehicles (AUVs) in different operations, the improvement of AUV motion control accuracy is the focus of this paper. In regard to the unsatisfying robustness of traditional control methods, an interactive network based on Least Square Support Vector Regression (LSSVR) is therefore put forward. The network completed the identification of the strong nonlinear AUV dynamic characteristics based on the LSSVR theory and by virtue of the interactions between the offline and online modules, it achieved offline design and online optimization of the AUV control law. In addition to contrastive numerical simulations and sea trials with the classic S-plane method in AUV velocity and heading control, the LSSVR network was also tested in path following and long-range cruise. The precision and robustness and of the proposed network were verified by the high-accuracy control results of the aforesaid simulations and trials. The network can be of practical use in AUV control especially under unfamiliar water conditions with access to a limited number of control samples or little information of the operation site.

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Section: Introductionmentioning

confidence: 99%

A LSSVR Interactive Network for AUV Motion Control

Jiang

Wan

Zhang

et al. 2023

JMSE

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Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators

Barhaghtalab,

Sepestanaki,

Mobayen

et al. 2023

Applied Soft Computing

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Trajectory Tracking of a 2-Degrees-of-Freedom Serial Flexible Joint Robot Using an Active Disturbance Rejection Controller Approach

Ramŕez-Neria,

Ochoa-Ortega,

Toro-Ossaba

et al. 2024

Mathematics

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This paper presents the development of an Active Disturbance Rejection Controller (ADRC) to address the trajectory tracking problem of a 2DOF (Degrees of Freedom) Serial Flexible Robot. The proposed approach leverages differential flatness theory to determine the system’s flat output, simplifying the trajectory tracking problem into a linear state feedback control with disturbance rejection. A set of a Generalized Proportional Integral Observer (GPIO) and Luenberger observers is employed to estimate the derivatives of the flat output and both internal and external disturbances in real time. The control law is experimentally validated on a 2DOF Serial Flexible Robot prototype developed by Quanser. Quantitative results demonstrate that the ADRC achieves superior performance compared to a partial state feedback control scheme, with a Mean Squared Error (MSE) as low as 1.0651 × 10−5 rad2 for trajectory tracking. The ADRC effectively suppresses oscillations, minimizes high-frequency noise and reduces saturation effects, even under external disturbances. These findings underscore the robustness and efficiency of the proposed method for underactuated flexible systems.

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An evolutionary intelligent control system for a flexible joints robot

Cited by 4 publications

References 47 publications

A LSSVR Interactive Network for AUV Motion Control

A LSSVR Interactive Network for AUV Motion Control

Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators

Trajectory Tracking of a 2-Degrees-of-Freedom Serial Flexible Joint Robot Using an Active Disturbance Rejection Controller Approach

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