The DOI matches its Crossref record.
An Approximation Theory of Optimal Control for Trainable Manipulators
Abstract
A theoretical procedure is developed for comparing the performance of arbitrarily selected admissible controls among themselves and with the optimal solution of a nonlinear optimal control problem. A recursive algorithm is proposed for sequential improvement of the control law which converges to the optimal. It is based on the monotonicity between the changes of the Hamiltonian and the value functions proposed by Rekasius, and may provide a procedure for selecting effective controls for nonlinear systems. The approach has been applied to the approximately optimal control of a trainable manipulator with seven degrees of freedom, where the controller is used for motion coordination and optimal execution of object-handling tasks.
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
(1995) · Cited by 10,558
Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems
Jiang, Y., & Jiang, Z.-P. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2294968 · Cited by 414
Adaptive linear controller for robotic manipulators
Koivo, A., & Guo, T.-H. (1983) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1983.1103211 · Cited by 331
Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems
Gao, W., & Jiang, Z.-P. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2761718 · Cited by 176
Metadata
- Authors
- Saridis, G. N., & Lee, C.-S. G.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics
- Published
- 1979
- Volume
- 9
- Issue
- 3
- Pages
- 152-159
- ISSN
- 0018-9472 (ISSN-L)
- OpenAlex
- W2083283377
- Cited by
- 348
- References
- 0
- FWCI
- 6.89
- 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: An Approximation Theory of Optimal Control for Trainable Manipulators. https://app.cochat.ai/papers/10.1109/tsmc.1979.4310171
- The paper
- Saridis, G. N., & Lee, C.-S. G. (1979). An Approximation Theory of Optimal Control for Trainable Manipulators. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/tsmc.1979.4310171
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.
