Function Approximation Technique-Based Adaptive Feedback Linearization Control for Compliant-Base Manipulators

Abstract

A compliant-base robotic manipulator presents an important control complication in the areas of aerospace, construction, and automation, resulting in a low ability of positioning because of the flexibility of the base, which is inherent.This study proposes an Adaptive Feedback Linearization (AFL) control scheme enhanced with a Function Approximation Technique (FAT) and a robust sliding term for base-induced disturbance rejection.Dynamics regarding the system have been defined using the Euler-Lagrange method, considering the base movement as the disturbance external to the system.The adaptive refinement of control law employs Chebyshev polynomial approximators, minimizing tracking error and control efforts.Closure of the Lyapunov theory renders stability under closed-loop conditions.Simulations have been done on a 2-DOF (degrees-of-freedom) manipulator mounted on a 1-DOF compliant base with rapid convergence in 3.7 seconds and low joint overshoot by 3.2%, vibration suppression with base displacement reduced below 0.001 m.Compared to classical controllers, the proposed FAT-AFL method reduced steady-state error by more than 80% and control effort by 65% and achieved superior damping without inducing chattering problems.These results suggest the applicability of the controller to high-precision and adaptive control purposes in realistic surroundings.