Hybrid adaptive fuzzy controllers of robot manipulators with bounds estimation
Abstract
A hybrid adaptive fuzzy controller comprises a weighted combination of a direct and an indirect adaptive fuzzy controller, with a continuously switched supervisory controller. The direct and indirect adaptive fuzzy controllers allow fuzzy control rules and fuzzy descriptions to be incorporated respectively, to achieve better adaptation speed. In the design of the supervisory controller, the bounds of the unknown robot dynamics are not required; they are adaptively estimated in the Lyapunov sense. Experimental evaluation conducted on a SEIKO TT-3000 SCARA robot shows that the proposed hybrid adaptive fuzzy controller is robust and stable. It also outperforms both the direct and indirect adaptive fuzzy controllers designed by the authors in their previous work in terms of tracking accuracy and the magnitude of control torque required.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [10]
Cites
Slotine, J.-J. E., & Li, W. (1991) · CERN Document Server (European Organization for Nuclear Research) · Cited by 18,937 · see citing works
Linear and nonlinear programming.
(1986) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(86)90092-3 · Cited by 4,806 · see citing works
Adaptive fuzzy systems and control
Wang, L. (1994) · Medical Entomology and Zoology · Cited by 908 · see citing works
Adaptive fuzzy systems and control
Pedrycz, W. (1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)91882-1 · Cited by 884 · see citing works
Linear and nonlinear programming
Berck, P., & Sydsæter, K. (1993) · Springer eBooks · doi:10.1007/978-3-662-11597-8_14 · Cited by 669 · see citing works
Adaptive fuzzy controllers of a robot manipulator
Neo, S. S., & Er, M. J. (1996) · International Journal of Systems Science · doi:10.1080/00207729608929246 · Cited by 36 · see citing works
Self-citationAdvances in fuzzy adaptive control
Esogbue, A. O., & Murrell, J. A. (1994) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(94)90120-1 · Cited by 17 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
- Er, M. J., & Chin, S. H.
- Affiliations
- Nanyang Technological University (SG)
- ORCID
- Meng Joo Er
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2000
- Volume
- 47
- Issue
- 5
- Pages
- 1151-1160
- ISSN
- 0278-0046 (ISSN-L)
- OpenAlex
- W2139452533
- Cited by
- 33
- References
- 7
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2022: 7 · 2020: 1 · 2019: 1 · 2014: 1
- FWCI
- 4.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Industrial Electronics (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 › Robotic Mechanisms and Dynamics
- Keywords
- robot manipulators, direct adaptive fuzzy control, indirect adaptive fuzzy control, supervisory controller, Lyapunov stability, SCARA robot, tracking accuracy, control torque
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
