Document Type : Original Article
Advanced Robotics and Automated Systems (ARAS), Faculty of Electrical Engineering K. N. Toosi University of Technology, Tehran, Iran.
Department of Mechanical Engineering, Robotics Laboratory, Laval University
Due to the complex model of cables, non-linearity, and uncertainties that exist in Cable-Driven Parallel Robots (CDPRs), this paper proposes a bio-inspired intelligent approach to overcome these challenges. This method, Brain Emotional Learning (BEL), mimics the emotional aspect of the mammal brain. Because of its easy-to-implement mathematical model, the Brain Emotional Learning-Based Intelligent Controller (BELBIC) brings fast adaptation, robustness, and low computational cost. The core idea of this paper is to define new saturated learning functions that eliminate the necessity of calculating the Jacobian matrix and forward kinematics in the control loop while still ensuring positive tensions. To evaluate the effectiveness of the proposed method, an experimental study was conducted using a plotter CDPR. The experimental results indicate that BELBIC can be adopted as a new approach in the trajectory tracking problem in the context of CDPRs, as it provides an acceptable tracking error (less than 10 degrees) without using the Jacobian matrix in the feedback loop.