The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Reinforcement Learning Control of a Flexible Two-Link Manipulator: An Experimental Investigation
Open accessAbstract
This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on actor–critic structure to enable vibration suppression while retaining trajectory tracking. Subsequently, the closed-loop system with the proposed RL control algorithm is proved to be semi-global uniform ultimate bounded (SGUUB) by Lyapunov’s direct method. In the simulations, the control approach presented has been tested on the discretized ODE dynamic model and the analytical claims have been justified under the existence of uncertainty. Eventually, a series of experiments in a Quanser laboratory platform are investigated to demonstrate the effectiveness of the presented control and its application effect is compared with PD control.
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
Neuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G., Sutton, R. S., & Anderson, C. W. (1983) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1983.6313077 · Cited by 3,233
Konda, V. R., & Tsitsiklis, J. N. (2002) · Cited by 2,202
Online actor–critic algorithm to solve the continuous-time infinite horizon optimal control problem
Vamvoudakis, K. G., & Lewis, F. L. (2010) · Automatica · doi:10.1016/j.automatica.2010.02.018 · Cited by 1,703
Reinforcement Learning and Dynamic Programming Using Function Approximators
Busoniu, L., Babuska, R., De Schutter, B., & Ernst, D. (2017) · CRC Press eBooks · doi:10.1201/9781439821091 · Cited by 919
Konda, V. R., & Tsitsiklis, J. N. (2003) · SIAM Journal on Control and Optimization · doi:10.1137/s0363012901385691 · Cited by 709
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
He, W., & Dong, Y. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2665581 · Cited by 642
Self-citationEvent-Triggered Adaptive Tracking Control for Multiagent Systems With Unknown Disturbances
Zhang, Y., Sun, J. Q., Liang, H., & Li, H. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2869084 · Cited by 380
Adaptive Fuzzy Backstepping Tracking Control for Strict-Feedback Systems With Input Delay
Li, H., Wang, L., Du, H., & Boulkroune, A. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2567457 · Cited by 347
Vibration Control of a Flexible Beam With Output Constraint
He, W., & Ge, S. S. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2400427 · Cited by 327
Self-citationPlatoon Formation Control With Prescribed Performance Guarantees for USVs
Dai, S.-L., He, S., Lin, H., & Wang, C. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2758743 · Cited by 327
Cited by
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
Optimized-Based Fault-Tolerant Control of an Electro-Hydraulic System with Disturbance Rejection
Phan, V. D., & Ahn, K. K. (2022) · Applied Sciences · doi:10.3390/app12189197 · Cited by 17
Metadata
- Authors
- He, W., Gao, H., Zhou, C., Yang, C., & Li, Z.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2021-12
- Volume
- 51
- Issue
- 12
- Pages
- 7326-7336
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W3009523569
- Cited by
- 284
- References
- 41
- FWCI
- 21.48
- 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: Reinforcement Learning Control of a Flexible Two-Link Manipulator: An Experimental Investigation. https://app.cochat.ai/papers/10.1109/tsmc.2020.2975232
- The paper
- He, W., Gao, H., Zhou, C., Yang, C., & Li, Z. (2021). Reinforcement Learning Control of a Flexible Two-Link Manipulator: An Experimental Investigation. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmc.2020.2975232
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.
