Neural-networks-based adaptive control of flexible robotic arms

Arciniegas, Jorge I.; Eltimsahy, A.H.; Cios, Krzysztof J.

doi:10.1016/s0925-2312(97)00037-4

Cited by 18 publications

(5 citation statements)

References 25 publications

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“…e adaptation laws help to find the controller gains as dynamic and time varying so as the tracking procedure would be modified [56][57][58][59][60]. Furthermore, the adaptation laws used in this paper apply the robust sliding mode concepts to produce the time-varying functions for the control gains (σ, δ , and as follows:…”

Section: Adaptation Lawsmentioning

confidence: 99%

Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode-Based Feedback Linearization Controller

Mahmoodabadi

Nejadkourki

2020

Journal of Robotics

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This work presents a novel fuzzy adaptive sliding mode-based feedback linearization controller for trajectory tracking of a flexible robot manipulator. To reach this goal, after deriving the dynamical equations of the robot, the feedback linearization approach is utilized to change the nonlinear dynamics to a linear one and find the control law. Then, the sliding mode control strategy is implemented to design a stabilizer for trajectory tracking of the flexible robot. In order to adaptively tune the parameters of the designed controller, the gradient descent approach and the chain derivative rule are employed. Moreover, the Takagi–Sugeno–Kang fuzzy system is applied to regulate the controller gains. Finally, a multiobjective particle swarm optimization algorithm is used to find the optimum fuzzy rules. The conflicting objective functions considered as the integrals of the absolute values of the state error and the control effort should be minimized, simultaneously. The simulation results illustrate the effectiveness and capability of the introduced scenario in comparison with other methods.

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Section: Adaptation Lawsmentioning

confidence: 99%

Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode-Based Feedback Linearization Controller

Mahmoodabadi

Nejadkourki

2020

Journal of Robotics

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“…RBF Neural Networks . RBF neural networks have been improved by many researchers and applied on many control problems [12][13][14][15][16][17].…”

Section: Manufacturing Science and Technology Icmst2011mentioning

confidence: 99%

Trajectory Tracking of a Spherical Robot Based on an RBF Neural Network

Zheng

Zhan

Liu

et al. 2011

AMR

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This paper deals with trajectory tracking problem of a spherical mobile robot, BHQ-1. First, a desired velocity is obtained by proposing a PD controller based on the kinematics. Then a PD controller with an RBF (Radial Basis Function) neural network is proposed based on the desired velocity and the inexact dynamics. The weights of the RBF network are designed with an adaptive rule based on the tracking error, and hence the network can compensate the uncertainties of the dynamics more effectively. Stability is presented via Lyapunov Theory and simulation results are provided to illustrate the tracking performance.

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“…For controlling robotic manipulators, Moosavian [6] used Transpose Jacobian (TJ) control. Arciniegas et al [7] developed neural network based adaptive control system to control the flexible robotic arm. Tseng [8] developed a DSP based instantaneous torque controller to control the manipulator.…”

Section: Introductionmentioning

confidence: 99%

Modeling a Controller for an Articulated Robotic Arm

Rajeev¹,

Koushik²,

Ravi³

2012

ICA

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The precise control upon each degree of freedom of a robotic arm is a great challenge in implementing industrial work. This paper aims to design a novel controller for an automated robotic arm. A discrete Proportional Integral Derivative (PID) control technique is being used to replace the complex electronic circuitry, which would greatly reduce the cost and size of the controller. DC motors will be controlled on the basis of Closed-loop System using an avr (Atmega 16/32) microcontroller. Transfer functions have been derived for mathematical modeling of the system through which the stability of the system can be evaluated prior to fabrication

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Neural-networks-based adaptive control of flexible robotic arms

Cited by 18 publications

References 25 publications

Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode-Based Feedback Linearization Controller

Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode-Based Feedback Linearization Controller

Trajectory Tracking of a Spherical Robot Based on an RBF Neural Network

Modeling a Controller for an Articulated Robotic Arm

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