Neural Network Based on Model Reference Using for Robot Arm Identification and Control

Khalil, Rafid Ahmed; Antar, Rakan Khalil

doi:10.33899/rengj.2014.101498

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…The neural network was trained to understand the complex behavior of robot arms. The outcomes of simulations on a model-based basis of a neural network for the identity and regulation of the robot's arm movement provide many close outcomes [90].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Review of Intelligent Control Systems with Robotics

Abbas

Mashhadany

Hameed

et al. 2022

IJEEI

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The interaction between humans and robots provides several important elements in improving the productivity of the tool in mechanical technology because the development of many complex tasks takes place through the application of multi-use robots technology that is self-adapted in these processes in various industrial or medical applications, which greatly contributed to the development of human life and achieving ways well-off Existing automated control frameworks have shaken up the construction business, making them highly adaptable and easy to use. This paper examines current and upcoming types of control frameworks and their implementation in mechanical technology, and the function of artificial intelligence in applying autonomy. In addition, he is expected to reveal insights into the various issues around control frameworks and the different approaches to fixing them. In addition, it proposes the basics of the application of autonomous control frameworks and various types of control frameworks in mechanical technology. Each type of control framework has its pros and cons which are discussed in this paper. Another type of robot control framework that upgrades the difficulty of the quest stage is man-made brainpower. Part of the speculations used in man-made reasoning, for example, artificial intelligence (AI) such as fuzzy logic, neural network, and genetic algorithm, are shown in this paper. Finally, a part of the joint effort between mechanical autonomy, personnel, and innovation is indicated. Coordinated human effort, for example, kinematic signal recognition used in games and versatile upper arm-based robots used in the clinical field for individuals with disabilities. Later on, it is only natural that the importance of different sensors is built up, thus expanding the knowledge and activity of the robot into a modern field. The data in this paper was collected based on many international references in publishing houses such as Springer, Elsevier, MDPI, IEEE, and others.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Review of Intelligent Control Systems with Robotics

Abbas

Mashhadany

Hameed

et al. 2022

IJEEI

View full text Add to dashboard Cite

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Model Reference‐Based Neural Controller for Transmission Line Inspection Robot

Karagöz,

Ekren,

Demir

et al. 2024

Journal of Field Robotics

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The regular inspection of the power transmission lines is essential for the uninterrupted transmission of electrical energy to demand points. This quickly requires actions with economically, efficiently, and safely. Therefore, the transmission line inspection robots are inevitable solution as an alternative to existing line inspection methods. This study present design and control of a transmission line inspection robot (I‐Robot). Since the I‐Robot exhibits nonlinear behavior and has multiple inputs and multiple outputs, a model reference‐based neural controller is determined to achieve nonlinear control. The robot design process consists of four stages which are kinematic modelling, dynamic modelling, actuator modelling and controller design. To meet inspection requirements, the conceptual design of the I‐Robot is performed, and the kinematic model are calculated in terms of the transformation matrices. According to the design requirements and system constraints, the dynamic model of the I‐Robot is created. To provide desired motions and trajectory tracking, the actuator models are determined. Then, the I‐Robot is prototyped. According to the dynamics of joint, robot and constraints, the system identification is performed to create reference model. During the system identification, the logged data are used the train the reference model. Finally, the desired trajectory for the driving cycles is created by manual excitation of the I‐Robot. During the manual excitation, the logged data are used to train the neural network (NN)‐based controller. Eventually, the I‐Robot is assessed under the test scenarios in term of the trajectory tracking performance as regression value and mean squared errors. According to the experiments, the neuron numbers and the training algorithm of the NN controller are determined. It was observed that the controller is quickly optimized with the adapting algorithm designed for the NN reference model. As a result, the performance of the model reference‐based neural controller was determined as 99%.

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Neural Network Based on Model Reference Using for Robot Arm Identification and Control

Cited by 2 publications

References 2 publications

Review of Intelligent Control Systems with Robotics

Review of Intelligent Control Systems with Robotics

Model Reference‐Based Neural Controller for Transmission Line Inspection Robot

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