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 21 publications

(4 citation statements)

References 13 publications

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“…Research along this line will continue to implement the control algorithm in real time and to further test it in a laboratory environment as well Two-Link [19], [23].…”

Section: Discussionmentioning

confidence: 99%

PD Control Law for Trajectory Tracking of Chaotic Pendulum

Perez¹,

Rdz²,

Flores³

2013

AMR

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This paper presents the application of trajectory tracking using the adaptive neural network to the double chaotic pendulum. The proposed controller structute is composed of a neural identifier and a PD Control. Experimental results with the chaotic pendulun shown the usefulness of the proposed approach. To verify the analytical results, an example of dynamical network is simulated and a theorem is proposed to ensure the tracking of the nonlinear system.

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“…Research along this line will continue to implement the control algorithm in real time and to further test it in a laboratory environment as well Two-Link [19], [23].…”

Section: Discussionmentioning

confidence: 99%

PD Control Law for Trajectory Tracking of Chaotic Pendulum

Perez¹,

Rdz²,

Flores³

2013

AMR

Self Cite

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“…Mustafa [17] presented a study using the three PID techniques for the control of the 2-Revelutejoint robot. A PID control law depends on neural networks and fuzzy PID controllers have been used in trajectory tracking control of two DoF robot arms [18]. Evolutionary algorithms (EAs) have emerged as an alternative design technique for robot motion control applications [19] and [20].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Trajectory Tracking Control of 2 DoFs SCARA Robot under External Force Acting to the Tip along the Trajectory

KARA,

YİĞİD,

ŞEN

et al. 2023

DÜMF MD

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The robot arms often follow a certain trajectory depending on the type of end effector with functions of spray-painting, arc welding, bonding or machining etc. Therefore, trajectory-tracking control is a very important issue in robot arm applications. Also, the robot must be able to follow the determined trajectory stably under the influence of external forces or machining forces it encounters in its operations. In this study, a Model Predictive Control (MPC) for trajectory tracking control of a 2 Degrees of Freedom (DoFs) Selective Compliant Assembly Robot Arm (SCARA) under an external force acting to the tip of the robot along the trajectory was performed. The effectiveness of the MPC method used has been demonstrated by simulation applications. According to simulation studies, successful results were obtained.

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

Cited by 21 publications

References 13 publications

PD Control Law for Trajectory Tracking of Chaotic Pendulum

PD Control Law for Trajectory Tracking of Chaotic Pendulum

Model Predictive Trajectory Tracking Control of 2 DoFs SCARA Robot under External Force Acting to the Tip along the Trajectory

Research on trajectory tracking control of 7-DOF picking manipulator

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