Welcome to Scholar Publishing Group

Kinetic Mechanical Engineering, 2023, 4(2); doi: 10.38007/KME.2023.040203.

Optimization of Control Method of Construction Machinery Manipulator Based on Neural Network


Ilankoon Raymond

Corresponding Author:
Ilankoon Raymond

Univ Adelaide, Adelaide, SA, Australia


Neural network is one of the important basic knowledge in computer, chemistry and information science, microelectronics technology and other fields. It has good universality in dealing with nonlinear system problems, and is widely used in industrial automation and intelligent control. This paper mainly introduces the mathematical model, algorithm design and implementation method of the construction machinery robot arm, and studies and analyzes its kinematics. Based on the neural network and artificial neuron theory, this paper constructs the robot arm position recognition technology in the object feature database for the characteristics and requirements of the subject. Finally, the function of the robot arm control model is tested, and the test results show that the arm support of the model has a long elongation distance, which can meet the production requirements.


Neural Network, Construction Machinery, Robot Arm, Control Method

Cite This Paper

Ilankoon Raymond. Optimization of Control Method of Construction Machinery Manipulator Based on Neural Network. Kinetic Mechanical Engineering (2023), Vol. 4, Issue 2: 19-26. https://doi.org/10.38007/KME.2023.040203.


[1] Anastasia Iskhakova, Daniyar Volf, Roman V. Meshcheryakov:Method for Reducing the Feature Space Dimension in Speech Emotion Recognition Using Convolutional Neural Networks. Autom. Remote. Control. 83(6): 857-868 (2022).  

[2] Hamid Abbasi, Mahdi Yaghoobi, Mohammad Teshnehlab, Arash Sharifi:Cascade chaotic neural network (CCNN): a new model. Neural Comput. Appl. 34(11): 8897-8917 (2022). 

[3] Alexander J. Dyer, Lewis D. Griffin:Inferring the location of neurons within an artificial network from their activity. Neural Networks 157: 160-175 (2023).  

[4] Bharat Mahaur, K. K. Mishra, Navjot Singh:Improved Residual Network based on norm-preservation for visual recognition. Neural Networks 157: 305-322 (2023). 

[5] Mohamed Abd-ElRahman Abdou:Literature review: efficient deep neural networks techniques for medical image analysis. Neural Comput. Appl. 34(8): 5791-5812 (2022).  

[6] Michael Opoku Agyeman, Andres Felipe Guerrero, Quoc-Tuan Vien:Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices. IEEE Access 10: 87387-87403 (2022). 

[7] Ebru Akis, Güral Güven, Bahram Lotfi Sadigh:Predictive models for mechanical properties of expanded polystyrene (EPS) geofoam using regression analysis and artificial neural networks. Neural Comput. Appl. 34(13): 10845-10884 (2022).  

[8] Qasem Abu Al-Haija, Ahmad Al Badawi:High-performance intrusion detection system for networked UAVs via deep learning. Neural Comput. Appl. 34(13): 10885-10900 (2022).  

[9] Andrea Alamia, Milad Mozafari, Bhavin Choksi, Rufin VanRullen:On the role of feedback in image recognition under noise and adversarial attacks: A predictive coding perspective. Neural Networks 157: 280-287 (2023).  

[10]  Driss El Alaoui, Jamal Riffi, Abdelouahed Sabri, Badraddine Aghoutane, Ali Yahyaouy, Hamid Tairi:Deep GraphSAGE-based recommendation system: jumping knowledge connections with ordinal aggregation network. Neural Comput. Appl. 34(14): 11679-11690 (2022).  

[11] Tristan Baumann, Hanspeter A. Mallot:Gateway identity and spatial remapping in a combined grid and place cell attractor. Neural Networks 157: 226-239 (2023). 

[12]  Chris Rohlfs:A descriptive analysis of olfactory sensation and memory in Drosophila and its relation to artificial neural networks. Neurocomputing 518: 15-29 (2023). 

[13] Adán Antonio Alonso-Ramírez, Tat'y Mwata-Velu, Carlos H. Garcia-Capulin, Horacio Rostro-González, Juan Prado-Olivarez, Marcos Gutierrez-Lopez, Alejandro Israel Barranco Gutiérrez:Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks. IEEE Access 10: 97348-97359 (2022).  

[14] Abdollah Amirkhani, Masoud Shirzadeh, Mahdi Molaie:An Indirect Type-2 Fuzzy Neural Network Optimized by the Grasshopper Algorithm for Vehicle ABS Controller. IEEE Access 10: 58736-58751 (2022).  

[15] Byeonghui Jeong, Seungyeon Baek, Sihyun Park, Jueun Jeon, Young-Sik Jeong:Stable and efficient resource management using deep neural network on cloud computing. Neurocomputing 521: 99-112 (2023).  

[16] Anaam Ansari, Tokunbo Ogunfunmi:Hardware Acceleration of a Generalized Fast 2-D Convolution Method for Deep Neural Networks. IEEE Access 10: 16843-16858 (2022).  

[17] Mohammadreza Ghorvei, Mohammadreza Kavianpour, Mohammad T. H. Beheshti, Amin Ramezani:Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis. Neurocomputing 517: 44-61 (2023). 

[18] Giosuè Cataldo Marinò, Alessandro Petrini, Dario Malchiodi, Marco Frasca:Deep neural networks compression: A comparative survey and choice recommendations. Neurocomputing 520: 152-170 (2023)