An evolutionary intelligent control system for a flexible joints robot

• Published in: Applied soft computing 2023-03, Vol.135, p.110043, Article 110043
• Main Authors: Pena, Alejandro, Tejada, Juan C., Gonzalez-Ruiz, Juan David, Sepúlveda-Cano, Lina María, Chiclana, Francisco, Caraffini, Fabio, Gongora, Mario
• Format: Article
• Language: English
• Subjects: Adaptive Neural Fuzzy Integrated Systems (ANFIS); Ornstein–Uhlenbeck (OU); Robotics; Stochastic model; System control
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2023.110043

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-Uhlenbeck 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. •An SF-ANFIS for the online control of RFJs is presented.•A Euler-Lagrange is proposed to model the persistence of disturbances in RFJs.•The low persistence of disturbances shows the stability of the SF-ANFIS control.•For maximum values, the SF-ANFIS integrates an EDA algorithm to guarantee stability.