Trajectory Tracking Error Using PID Control Law for Two-Link Robot Manipulator via Adaptive Neural Networks

Peréz, P.; Perez, Jose P.; Soto, Rogelio; Flores, Angel; Rodríguez, Fernando Martín; Meza, José Luis

doi:10.1016/j.protcy.2012.03.015

Cited by 19 publications

(2 citation statements)

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“…The current trajectory tracking control methods mainly include calculation torque method, fuzzy control, neural network control, and fuzzy neural network control methods. [11][12][13][14] The calculated moment method is mainly used when the model is relatively fixed, Fuzzy logic systems have the ability to approximate arbitrary nonlinear functions with arbitrary accuracy. 15 In recent years, adaptive fuzzy control has been increasingly used in manipulator control and has achieved a series of results.…”

Section: Introductionmentioning

confidence: 99%

Research on trajectory tracking control of 7-DOF picking manipulator

Sun

Cao

2021

Science Progress

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In order to solve the problem of poor robustness of the traditional method of calculating torque in the mechanical model of 7-DOF picking manipulator, this paper proposes a control strategy of calculating torque plus fuzzy compensation by using adaptive fuzzy logic system to compensate the uncertain part of the mechanical model of 7-DOF picking manipulator. By using Lagrange method, the dynamic model of 7-DOF manipulator is established, and the relationship between joint motion and applied torque (force) is obtained. Using ADAMS and MATLAB to establish a co-simulation platform, the manipulator and trajectory tracking control system are simulated. The results show that the trajectory tracking error of each joint in the algorithm is obviously reduced and the convergence trend is obvious. The average trajectory tracking accuracy of joint 1 to joint 7 was improved by 70.22%, 94.78%, 0.62%, 74.23%, 89.78%, 86.45%, and 67.15%, respectively. In this control scheme, the control force (moment) of each joint changes regularly, and the output force (moment) does not appear chattering and mutation when the disturbance signal is added. The research results can provide support for the further study of picking manipulator trajectory tracking control system.

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Section: Introductionmentioning

confidence: 99%

Research on trajectory tracking control of 7-DOF picking manipulator

Sun

Cao

2021

Science Progress

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“…So that, designing a controller by the traditional control methods that depend on the robotic system dynamics is a very difficult task. Different control schemes have been provided for the two link manipulator robotic system such as Joel Perez et al [1]. Perez has introduced a PID control law that depends on neural networks.…”

Section: Introductionmentioning

confidence: 99%

Novel PID Tracking Controller for 2DOF Robotic Manipulator System Based on Artificial Bee Colony Algorithm

Elkhateeb

Badr

2017

Electrical, Control and Communication Engineering

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-This study presents a well-developed optimization methodology based on the dynamic inertia weight Artificial Bee Colony algorithm (ABC) to design an optimal PID controller for a robotic arm manipulator. The dynamical analysis of robotic arm manipulators investigates a coupling relation between the joint torques applied by the actuators and the position and acceleration of the robot arm. An optimal PID control law is obtained from the proposed (ABC) algorithm and applied to the robotic system. The designed controller optimizes the trajectory of the robot's end effector for a time-variant input and makes the robot robust in the presence of external disturbance.

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