An inverse dynamics based fuzzy adaptive state-feedback controller for a nonlinear 3DOF manipulator

Mahmoodabadi, Mohammad Javad; Nejadkourki, N.

doi:10.1080/02286203.2023.2168475

Cited by 1 publication

(1 citation statement)

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“…It was found that the fuzzy controller based on inverse dynamics provides a near-zero steady state error. In the study in [9], a new fuzzy adaptive state-feedback controller based on dynamics was introduced to control the joint positions for the stabilization of a 3-DoF robot manipulator. In this work, the proposed strategy showed some advantages such as a shorter settling time and more robustness, in comparison with other controllers.…”

Section: Introductionmentioning

confidence: 99%

Design, Simulation, Implementation, and Comparison of Advanced Control Strategies Applied to a 6-DoF Planar Robot

Urrea

Saa

2023

Symmetry

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In general, structures with rotational joints and linearized dynamic equations are used to facilitate the control of manipulator robots. However, in some cases, the workspace is limited, which reduces the accuracy and performance of this type of robot, especially when uncertainties are considered. To counter this problem, this work presents a redundant planar manipulator robot with Six-Degree-of-Freedom (6-DoF), which has an innovative structural configuration that includes rotary and prismatic joints. Three control strategies are designed for the monitoring and regulation of the joint trajectory tracking problem of this robot under the action of variable loads. Two advanced control strategies—predictive and Fuzzy-Logic Control (FLC)—were simulated and compared with the classical Proportional–Integral–Derivative (PID) controller. The graphic simulator was implemented using tools from the MATLAB/Simulink software to model the behavior of the redundant planar manipulator in a virtual environment before its physical construction, in order to conduct performance tests for its controllers and to anticipate possible damages/faults in the system mechanics before the implementation of control strategies in a real robot. The inverse dynamics were obtained through the Lagrange–Euler (L-E) formulation. According to the property of symmetry, this model was obtained in a simplified way based on the main diagonal of the inertia matrix of the robot. Additionally, the model includes the dynamics of the actuators and the estimation of the friction forces, both with central symmetry present in the joints. The effectiveness of these three control strategies was validated through qualitative comparisons—performance graphs of trajectory tracking—and quantitative comparisons—the Common Mode Rejection Ratio (CMRR) performance indicator and joint error indexes such as the Residual Mean Square (RMS), Residual Standard Deviation (RSD), and Index of Agreement (IA). In this regard, FLC based on the dynamic model was the most-suitable control strategy.

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Section: Introductionmentioning

confidence: 99%