System identification and control using genetic algorithms
Abstract
It is shown how genetic algorithms can be applied for system identification of both continuous and discrete time systems. It is shown that they are effective in both domains and are able to directly identify physical parameters or poles and zeros. This can be useful because changing one physical parameter might affect every parameter of a system transfer function. The estimates of poles and zeros are then used to design a discrete time pole placement adaptive controller. Simulations for minimum and nonminimum phase systems and a system with unmodeled dynamics are presented.>
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?
In classical robotics, the mismatch caused by the dynamic model is partially mitigated through system identification that calibrates the simulator using real-world data.
“In classical robotics, the mismatch caused by the dynamic model is partially mitigated through system identification [83] that calibrates the simulator using real-world data.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [83]
Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,169 · see citing works
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340 · see citing works
Theory and Practice of Recursive Identification
Ljung, L., & Söderström, T. (1983) · Lund University Publications (Lund University) · Cited by 3,995 · see citing works
Genetic Algorithms and Simulated Annealing
Davis, L. D. (1987) · Cited by 1,758 · see citing works
An Analysis Of The Behavior Of A Class Of Genetic Adaptive Systems.
DeJong, K. A. (1975) · Deep Blue (University of Michigan) · doi:10.7302/10966 · Cited by 1,299 · see citing works
Outline for a Logical Theory of Adaptive Systems
Holland, J. H. (1962) · Journal of the ACM · doi:10.1145/321127.321128 · Cited by 1,117 · see citing works
Adaptive Selection Methods for Genetic Algorithms
Baker, J. E. (1985) · international conference on Genetic algorithms · Cited by 989 · see citing works
Genetic Algorithms for the Traveling Salesman Problem
Grefenstette, J. J., Gopal, R., Rosmaita, B. J., et al. (1985) · international conference on Genetic algorithms · Cited by 731 · see citing works
Alleles, loci and the traveling salesman problem
Goldberg, D. E., & Lingle, R. (1985) · international conference on Genetic algorithms · Cited by 682 · see citing works
Perdikaris, G. A. (1991) · Springer eBooks · doi:10.1007/978-94-015-7929-2_6 · Cited by 505 · see citing works
Genetic Algorithms as Function Optimizers.
Bethke, A. D. (1980) · Deep Blue (University of Michigan) · Cited by 223 · see citing works
Self-tuning control of nonminimum-phase systems
CLARKE, D. W. (1984) · Automatica · doi:10.1016/0005-1098(84)90003-7 · Cited by 209 · see citing works
Adaptive forgetting in recursive identification through multiple models†
ANDERSSON, P. (1985) · International Journal of Control · doi:10.1080/00207178508933420 · Cited by 166 · see citing works
A genetic algorithmic framework for process design and optimization
Androulakis, I. P., & Venkatasubramanian, V. (1991) · Computers & Chemical Engineering · doi:10.1016/0098-1354(91)85009-j · Cited by 119 · see citing works
Recursive adaptive filter design using an adaptive genetic algorithm
Etter, D. M., Hicks, M., & Cho, K. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1982.1171777 · Cited by 103 · see citing works
A Comparative Study of Recursive Identification Methods
Söderström, T., Ljung, L., & Gustavsson, I. (1974) · Lund University Publications (Lund University) · Cited by 92 · see citing works
Genetic algorithms in system identification
Kristinsson, K., & Dumont, G. A. (2003) · IEEE International Symposium on Intelligent Control · doi:10.1109/isic.1988.65498 · Cited by 26 · see citing works
Self-citationIdentification of continuous-time dynamic systems
Johansson, R. G. (1986) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1986.267189 · Cited by 15 · see citing works
Genetic algorithms in system identification and control
Kristinsson, K. (2010) · cIRcle (University of British Columbia) · doi:10.14288/1.0065632 · Cited by 4 · see citing works
Self-citationAn Adaptive Pole Placement Controller Based on Pole-zero Parameterization
Wittenmark, B., & Evans, R. J. (1988) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)54709-6 · Cited by 1 · see citing works
On identification of hydraulic compliance using a genetic algorithm
Wan, F. L. K., Sepehri, N., Kristinsson, K., et al. (1991) · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
Zhao, W., Queralta, J. P., & Westerlund, T. (2020) · 2020 IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308468 · Cited by 941 · see citing works
Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works
“In classical robotics, the mismatch caused by the dynamic model is partially mitigated through system identification [83] that calibrates the simulator using real-world data.”
Method
- Authors
- Kristinsson, K., & Dumont, G. A.
- Affiliations
- University of British Columbia (CA)
- ORCID
- Dumont, G.A.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics
- Published
- 1992
- Volume
- 22
- Issue
- 5
- Pages
- 1033-1046
- ISSN
- 0018-9472 (ISSN-L)
- OpenAlex
- W2146468668
- Cited by
- 517
- References
- 21
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 2 · 2023: 12 · 2022: 4 · 2021: 8 · 2020: 10 · 2019: 8 · 2018: 3 · 2017: 8 · 2016: 13 · 2015: 7 · 2014: 22 · 2013: 8 · 2012: 20
- FWCI
- 18.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Design
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Keywords
- genetic algorithm, system identification, continuous-time systems, discrete-time systems, parameter estimation, pole placement control, adaptive control, non-minimum phase systems, unmodeled dynamics
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
