Adjustable backstepping fuzzy controller for a 7 DOF anthropomorphic manipulator

Abstract

Various control methods for controlling robotic manipulators have emerged as a promising direction in the field of robotic control over the past decade. Researchers are exploring a diverse range of classical and modern techniques to address the challenging issue of time variant nonlinearities and uncertainties in manipulator dynamics. In this paper, we review and discuss several classical control methods for robotic manipulators such as model-free method,pure inverse dynamic method, andfuzzy logic-based method, to apply on a 7-degrees-of-freedom anthropomorphic manipulator. We compare and contrast the performance of these methods with oursuggested method of optimization through a series of simulation tests. An adjustable backstepping fuzzy controller is suggested, which introduces new variables into the system, where a nonlinear fuzzy filter creates a dynamic nonlinear estimator that is more robust to uncertainties, nonlinearities, and external disturbances than pure feedback linearization controllers, and decreases trial and error which makes the tuning process easier.In the suggested control law Gaussian membership functions were used instead of triangular ones.Our results demonstrate that the proposed error-based backstepping fuzzy controller can achieve high accuracy and speed, even in the presence of significant disturbances. This makes it a promising candidate for use in robotic applications where high performance is required, such as the tasks of grasping objects which is based on pattern recognition.