Work

A New Motion Tracking Controller with Feedforward Compensation for Robot Manipulators Based on Sectorial Fuzzy Control and Adaptive Neural Networks

Year: 2025

Type: article

Abstract: A novel trajectory tracking control approach for robot manipulators that uses adaptive neural network feedforward compensation plus a sectorial fuzzy controller is presented. We conduct simulation and real-time experiments comparing it with two previously published control schemes: a Proportional–Derivative (PD) plus feedforward compensation controller model, and a sectorial fuzzy control plus feedforward compensation model. The proposed controller shows a faster transient response and better steady-state angular error performance than its counterparts, and it maintains its tolerance to parameter deviation, a main characteristic of fuzzy controllers; furthermore, it excludes the need for knowledge of the robot manipulator model to achieve excellent results. A formal stability analysis of the proposed controller in a closed loop with the robot manipulator guarantees that position and velocity errors converge to zero and all signals are uniformly bounded.

Source: Mathematics

DOI: 10.3390/math13060977

Authors: Andres Pizarro-Lerma, Victor Santibañez, Ramon Garcia-Hernandez, Jorge Villalobos-Chin, Javier Moreno-Valenzuela

Institutions: Sonora Institute of Technology, Tecnológico Nacional de México, Instituto Tecnólogico de La Laguna, Universidad Juárez del Estado de Durango, Instituto Politécnico Nacional

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Tecnológico Nacional de México