The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model
Open accessAbstract
In this paper, automatic motion control is investigated for one of wheeled inverted pendulum (WIP) models, which have been widely applied for modeling of a large range of two wheeled modern vehicles. First, the underactuated WIP model is decomposed into a fully actuated second order subsystem Σa consisting of planar movement of vehicle forward and yaw angular motions, and a nonactuated first order subsystem Σb of pendulum motion. Due to the unknown dynamics of subsystem Σa and the universal approximation ability of neural network (NN), an adaptive NN scheme has been employed for motion control of subsystem Σa . The model reference approach has been used whereas the reference model is optimized by the finite time linear quadratic regulation technique. The pendulum motion in the passive subsystem Σb is indirectly controlled using the dynamic coupling with planar forward motion of subsystem Σa , such that satisfactory tracking of a set pendulum tilt angle can be guaranteed. Rigours theoretic analysis has been established, and simulation studies have been performed to demonstrate the developed method.
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
Cites
Slotine, J.-J. E., & Li, W. (1991) · CERN Document Server (European Organization for Nuclear Research) · Cited by 18,975
Universal Approximation Using Radial-Basis-Function Networks
Park, J., & Sandberg, I. W. (1991) · Neural Computation · doi:10.1162/neco.1991.3.2.246 · Cited by 4,112
Neural Network Control Of Robot Manipulators And Non-Linear Systems
Lewis, F. W., Jagannathan, S., & Yesildirak, A. (2020) · CRC Press eBooks · doi:10.1201/9781003062714 · Cited by 1,867
Small-gain theorem for ISS systems and applications
Jiang, Z. P., Teel, A. R., & Praly, L. (1994) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf01211469 · Cited by 1,366
Structural properties and classification of kinematic and dynamic models of wheeled mobile robots
Campion, G., Bastin, G., & Dandrea-Novel, B. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.481750 · Cited by 1,192
Chen, M., Ge, S. S., & How, B. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2042611 · Cited by 732
Adaptive Neural Network Control of Robotic Manipulators
Ge, S. S., Lee, T. H., & Harris, C. J. (1998) · World Scientific Series in Robotics and Intelligent Systems · doi:10.1142/3774 · Cited by 618
Adaptive output feedback control of nonlinear systems using neural networks
Calise, A. J., Hovakimyan, N., & Idan, M. (2001) · Automatica · doi:10.1016/s0005-1098(01)00070-x · Cited by 449
Liu, Y.-J., Chen, C. L. P., Wen, G.-X., & Tong, S. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2146788 · Cited by 366
Persistency of Excitation in Identification Using Radial Basis Function Approximants
Kurdila, A. J., Narcowich, F. J., & Ward, J. D. (1995) · SIAM Journal on Control and Optimization · doi:10.1137/s0363012992232555 · Cited by 319
Cited by
Adaptive Neural Control of Uncertain Nonlinear Systems Using Disturbance Observer
Chen, M., Shao, S.-Y., & Jiang, B. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2667680 · Cited by 292
Cao, S., Sun, L., Jiang, J., & Zuo, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3116713 · Cited by 221
Metadata
- Authors
- Yang, C., Li, Z., Cui, R., & Xu, B.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2014-11
- Volume
- 25
- Issue
- 11
- Pages
- 2004-2016
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2074833352
- Cited by
- 305
- References
- 17
- FWCI
- 41.31
- Type
- journal article
- Language
- en
- Open access
- green
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: Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model. https://app.cochat.ai/papers/10.1109/tnnls.2014.2302475
- The paper
- Yang, C., Li, Z., Cui, R., & Xu, B. (2014). Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2014.2302475
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.
