The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion
Abstract
In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller.
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“If the conditions described in Eqs. (20) and (21) are both satisfied, then the system is considered to be asymptotically stable [27, 28].”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [28]
Citations
Cites
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Flagged first, then by citations
Research works printed but not linked: 2
- Jia C, Li X, Wang K, Ding D. Adaptive control of nonlinear system using online error minimum neural networks, ISA transactions.
- Haykin, 2004, A comprehensive foundation, Neural Netw, 2
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Items 101–102 of 102
Możaryn, J., Guś, B., & Winnicki, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-32213-5_17
Hanna, Y. F., El-Nagar, A. M., El-Bardini, M. A., & Khater, A. A. (2026) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-026-00591-2
Metadata
- Authors
- Kumar, R., Srivastava, S., & Gupta, J. R. P.
- Affiliations
- Netaji Subhas University of Technology (IN)
- Corresponding author
- Kumar, Rajesh
- Venue
- ISA Transactions
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 67
- Pages
- 407-427
- ISSN
- 0019-0578 (ISSN-L)
- OpenAlex
- W2580486137
- Cited by
- 102
- References
- 61
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 18 · 2023: 6 · 2022: 13 · 2021: 5 · 2020: 9 · 2019: 13 · 2018: 6 · 2017: 2
- FWCI
- 5.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ISA Transactions (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- diagonal recurrent neural network, adaptive control, nonlinear dynamical systems, Lyapunov stability criterion, inverted pendulum
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