Fuzzy-Neural-Network Inherited Sliding-Mode Control for Robot Manipulator Including Actuator Dynamics
Abstract
This paper presents the design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link robot manipulator, including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness. First, the coupled higher order dynamic model of an n-link robot manipulator is briefy introduced. Then, a conventional SMC scheme is developed for the joint position tracking of robot manipulators. Moreover, a fuzzy-neural-network inherited SMC (FNNISMC) scheme is proposed to relax the requirement of detailed system information and deal with chattering control efforts in the SMC system. In the FNNISMC strategy, the FNN framework is designed to mimic the SMC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNISMC system, and the superiority of the proposed FNNISMC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.
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Intelligent control techniques such as fuzzy logic systems and neural networks are proposed to approximate unknown or uncertain system dynamics, but they focus on robustness rather than optimal tracking performance, and input and state constraints are usually not of interest.
“To alleviate the need of concrete mathematical modeling and parameter identification, intelligent control techniques, such as fuzzy logic systems [10] and neural networks (NNs) [11], are proposed to approximate unknown/uncertain system dynamics. Yet, these approaches also pay more attention to robustness, not to optimal tracking performance. In addition, input and state constraints are usually not”
From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [11]
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Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator
Wang, Y., Leibold, M., Lee, J., Ye, W., Xie, J., & Buss, M. (2022) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2022.3142629 · Cited by 52 · see citing works
- Authors
- Wai, R.-J., & Muthusamy, R.
- Affiliations
- Yuan Ze University (TW)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2013-02
- Volume
- 24
- Issue
- 2
- Pages
- 274-287
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2050918738
- Cited by
- 188
- References
- 39
- Citations by year
- 2025: 7 · 2024: 11 · 2023: 8 · 2022: 17 · 2021: 22 · 2020: 13 · 2019: 19 · 2018: 28 · 2017: 19 · 2016: 8 · 2015: 13 · 2014: 17 · 2013: 6
- FWCI
- 16.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Dynamics and Control of Mechanical Systems
- Keywords
- sliding mode control, robot manipulators, actuator dynamics, joint position tracking, chattering reduction, projection algorithm, Lyapunov stability, DC servo motor, two-link manipulator
- MeSH terms
- Robotics, Neural Networks, Computer, Fuzzy Logic, Nonlinear Dynamics
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