An adaptive Neuro-Fuzzy control approach for motion control of a robot arm

Lakshmi, K.; Nabi, M.

doi:10.1109/iciev.2012.6317522

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…A stable position of the cannon on the warship is effected by the warship movement and the ocean wave. This has the same concept as the stability of a robot arm implemented using the neural network method as in [10]. Robot as an agent must act based on the environment.…”

Section: A Neural Network Controlmentioning

confidence: 99%

The Performance of Controlling Cannon Barrel Position on the Moving Platform Using Neural Network Control and Sliding Mode Control

Wiharti¹,

Anggraini²,

Rimra³

2015

IJCTE

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Abstract-One of the gunboat weapons that need to stay stable is the cannon. Its unbalance position that caused by pitch and roll disturbance will influence the target accuracy, target detection, tracking system, object identification and the ability to counter the threat. In order to determine this disturbance, the balancing control on the movement platform can be solved by using neural network control and sliding mode control methods. To make an approach, the cannon movement system can be modeled in training and elevation movements and the disturbances are modeled through pitch and roll mechanisms. The variations in obtained parameters of training and elevation (moment of inertia) are the non-linearity result of the moving cannon. The system is simulated to verify the error in the controller's output processed using the neural network coordination system control and sliding mode control. The learning process in the neural network is made using back propagation method in order to get the weight value at the different disturbances which their results are given in the simulation of coordination models. On the other hand, the free chattering of sliding mode control is implemented in order to make the movement of training and elevation can be controlled for having the desired angle position in the disturbance of pitch and roll. This paper is based on the study to compare the performance of neural network control and sliding mode control on the moving platform.Index Terms-Cannon barrel, elevation and training, neural network control, pitch and roll, sliding mode control.

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Section: A Neural Network Controlmentioning

confidence: 99%

The Performance of Controlling Cannon Barrel Position on the Moving Platform Using Neural Network Control and Sliding Mode Control

Wiharti¹,

Anggraini²,

Rimra³

2015

IJCTE

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“…Learning Environment For Robotic Arm Using a teach pendant or a human-computer interface (HCI), [21] direct teaching techniques entail physically moving the robotic arm. Robot motions are manually directed by operators, who record the trajectories for later replay.…”

Section: Introductionmentioning

confidence: 99%

Design and Control of Advanced Robotic Systems for Manufacturing and Assembly

Verma¹

2021

MSEA

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Modern industrial processes must now be designed and controlled with advanced robotic systems for manufacture and assembly to increase productivity, accuracy, and flexibility. The main factors involved in creating and overseeing these robotic systems are presented in detail in this work.In the design stage, manipulators, sensors, and end-effectors that are customised for the particular manufacturing or assembly task are chosen and integrated. To ensure accurate and efficient task execution, additional control algorithms and programming techniques are also developed. This covers adaptive control techniques, path optimisation, trajectory planning, and collision avoidance.The real-time monitoring, coordinating, and synchronisation of several robots and their interactions with the environment are the main goals of the control component. The integration of human operators with robotic systems is made possible by centralised or decentralised control frameworks, which promote secure and effective production and assembly procedures.The study also discusses how to improve robotic systems' abilities to handle complicated jobs and adapt to shifting production settings by integrating cutting-edge technologies like machine learning, computer vision, and augmented reality.The difficulties and possibilities posed by the application of sophisticated robotic systems for manufacturing and assembly are also covered. These include the dependability of the system, the ethical effects of automation in the workplace, the detection and recovery of faults, and human-robot collaboration.The ultimate goal of this work is to give readers a thorough grasp of the design and control principles required for creating advanced robotic systems for manufacturing and assembly, hence promoting higher levels of productivity, quality, and adaptability in industrial settings.

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“…His approach requires ANN to identify the complete robot inverse dynamics for compensation. Lakshmi and Mashuq [2], have also introduced an adaptive Neuro-Fuzzy control method. This is for Cartesian motion control of a 4-DOF robot arm.…”

Section: Introductionmentioning

confidence: 99%

Neural Net Control of High Nonlinear 2-DOF Nonlinear Robotic Arm

2015

2015 the 5th International Workshop on Computer Science and Engineering-Information Processing and Control Engineering

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A robust ANN system to enhance the control performance and robustness of a Two links robotic system (UoB-Arm) is presented. The UoB-Arm control architecture consists of a classical (PD) controller, independent of the high nonlinear dynamics of the two links robotic model, in parallel with a trainable ANN architecture. ANN architecture is used to identify the whole robotic arm inverse dynamics for arm control compensation. The (PD) controller is used to guarantee the stability and robustness of the whole control system, hence achieve a pré cised tracking accuracy.

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An adaptive Neuro-Fuzzy control approach for motion control of a robot arm

Cited by 7 publications

References 12 publications

The Performance of Controlling Cannon Barrel Position on the Moving Platform Using Neural Network Control and Sliding Mode Control

The Performance of Controlling Cannon Barrel Position on the Moving Platform Using Neural Network Control and Sliding Mode Control

Design and Control of Advanced Robotic Systems for Manufacturing and Assembly

Neural Net Control of High Nonlinear 2-DOF Nonlinear Robotic Arm

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