Comparison of Adaptive and Robust Approaches in the Context of Feedback Linearization

Most real-world systems exhibit nonlinear dynamics, limiting the effectiveness of classical control techniques and potentially causing instability. These characteristics motivate the adoption of nonlinear control methods, such as feedback linearization, which are widely applied in robotics, aviation, and energy. However, its performance degrades under model uncertainties. Learning-based approaches like evolving Takagi-Sugeno (eTS) and evolving Participatory Learning (ePL) algorithms have been integrated to address this. This study investigates two enhanced strategies: Robust Granular Feedback Linearization (RGFL), which couples feedback linearization with ePL, and Limbic System-Inspired Control (LISIC), based on biological principles. A literature review and simulation-based evaluation of an inverted pendulum system are presented, highlighting each technique’s robustness and distinct characteristics.