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Li J, Su J, Yu W, Mao X, Liu Z, Fu H. Recurrent neural network for trajectory tracking control of manipulator with unknown mass matrix. Front Neurorobot 2024; 18:1451924. [PMID: 39224905 PMCID: PMC11366699 DOI: 10.3389/fnbot.2024.1451924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Accepted: 07/22/2024] [Indexed: 09/04/2024] Open
Abstract
Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a given task with error feedback. Subsequently, integrated with the kinematics controller, the RNN is proposed to combine the robot's dynamic model and a mass matrix estimator. This integration allows the manipulator system to handle uncertainties and synchronously achieve trajectory tracking effectively. Theoretical analysis demonstrates the learning and control capabilities of the RNN. Simulative experiments conducted on a Franka Emika Panda manipulator, and comparisons validate the effectiveness and superiority of the proposed method.
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Affiliation(s)
| | | | | | | | | | - Haitao Fu
- College of Information Technology, Jilin Agricultural University, Changchun, China
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Li H, Liao B, Li J, Li S. A Survey on Biomimetic and Intelligent Algorithms with Applications. Biomimetics (Basel) 2024; 9:453. [PMID: 39194432 DOI: 10.3390/biomimetics9080453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/12/2024] [Accepted: 07/22/2024] [Indexed: 08/29/2024] Open
Abstract
The question "How does it work" has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in tackling optimization problems with intelligence algorithms. First, a special neural network was comprehensively discussed, and it was called a zeroing neural network (ZNN). It is especially intended for solving time-varying optimization problems, including origin, basic principles, operation mechanism, model variants, and applications. This paper presents a new classification method based on the performance index of ZNNs. Then, two classic bio-inspired algorithms, a genetic algorithm and a particle swarm algorithm, are outlined as representatives, including their origin, design process, basic principles, and applications. Finally, to emphasize the applicability of intelligence algorithms, three practical domains are introduced, including gene feature extraction, intelligence communication, and the image process.
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Affiliation(s)
- Hao Li
- College of Computer Science and Engineering, Jishou University, Jishou 416000, China
- School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China
| | - Bolin Liao
- College of Computer Science and Engineering, Jishou University, Jishou 416000, China
| | - Jianfeng Li
- College of Computer Science and Engineering, Jishou University, Jishou 416000, China
| | - Shuai Li
- College of Computer Science and Engineering, Jishou University, Jishou 416000, China
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Maurya BM, Yadav N, T A, J S, A S, V P, Iyer M, Yadav MK, Vellingiri B. Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges. CHEMOSPHERE 2024; 353:141474. [PMID: 38382714 DOI: 10.1016/j.chemosphere.2024.141474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/17/2024] [Accepted: 02/14/2024] [Indexed: 02/23/2024]
Abstract
Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies.
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Affiliation(s)
- Brij Mohan Maurya
- Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Nidhi Yadav
- Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Amudha T
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - Satheeshkumar J
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - Sangeetha A
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - Parthasarathy V
- Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Pollachi Main Road, Eachanari Post, Coimbatore, 641021, Tamil Nadu, India
| | - Mahalaxmi Iyer
- Centre for Neuroscience, Department of Biotechnology, Karpagam Academy of Higher Education, Coimbatore, 641021, Tamil Nadu, India; Department of Microbiology, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Mukesh Kumar Yadav
- Department of Microbiology, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Balachandar Vellingiri
- Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, Punjab, India.
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