The DOI matches its Crossref record.
Inversion of multivariable linear systems
Abstract
A new algorithm for constructing an inverse of a multivariable linear dynamical system is presented. This algorithm, which is considerably more efficient than previous methods, also incorporates a relatively simple criterion for determining if an inverse system exists. New insight into the structure of a system inverse is gained by consideration of the inverse system representations resulting from the algorithm. A precise bound on the number of output differentiations required is obtained as well as a bound on the total number of integrators and differentiators necessary to realize the inverse. This latter bound is equal to the order of the original system. A further advantage of the algorithm and theory developed is that it is applicable to both time-invariant systems and time-variable systems which satisfy certain regularity conditions. One application is also given: a complete description of the set of initial states necessary and sufficient for a specified function to be the output of an invertible system.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Levin, A. U., & Narendra, K. S. (1996) · IEEE Transactions on Neural Networks · doi:10.1109/72.478390 · Cited by 441
Iterative learning control for discrete-time systems with exponential rate of convergence
Amann, N., Owens, D. H., & Rogers, E. (1996) · IEE Proceedings - Control Theory and Applications · doi:10.1049/ip-cta:19960244 · Cited by 406
Attack detection and identification for automatic generation control systems
Ameli, A., Hooshyar, A., El-Saadany, E. F., & Youssef, A. M. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2810161 · Cited by 222
Metadata
- Authors
- Silverman, L.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1969-06
- Volume
- 14
- Issue
- 3
- Pages
- 270-276
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2094429957
- Cited by
- 813
- References
- 0
- FWCI
- 34.54
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Inversion of multivariable linear systems. https://app.cochat.ai/papers/10.1109/tac.1969.1099169
- The paper
- Silverman, L. (1969). Inversion of multivariable linear systems. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.1969.1099169
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.