Intelligent Fuzzy Logic-Based Internal Model Control for Rotary Flexible Robots

Recently, there has been widespread and vital adoption of flexible manipulators due to their increased prevalence. This is attributed to the growing demand for flexibility in various tasks like refueling operations, inspections, and maintenance activities. Nevertheless, these robots are under-actuated systems characterized by a nonlinear behavior and present dynamic coupling interactions that contribute to the complexity of the control process. The main control objective is to achieve an accurate tracking of the desired position while simultaneously reducing oscillations occurring in the link. Therefore, this paper proposes integrating the tuning and adaptive control by employing fuzzy logic methodology in conjunction with internal model control (IMC). The suggested controller takes advantage of intelligent techniques, simple structure, robustness, and easy tuning of the conventional IMC. Both triangular and trapezoidal Membership Functions (MFs) are applied in this study to create a pair of Fuzzy Logic Controllers (FLCs) based on the Mamdani method. These controllers are employed to dynamically adjust the parameters of the IMC, in contrast to the fixed parameters used in the conventional IMC approach. The effectiveness of the suggested Adaptive-based Fuzzy IMC (AFIMC) is showcased through simulation and practical experimentation, in scenarios both with and without disturbances. Results indicate that this technique outperforms conventional IMC in achieving control objectives and rejecting disturbances.