Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.3390/electronics12244995
Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12244995
- https://www.mdpi.com/2079-9292/12/24/4995/pdf?version=1702465883
- OA Status
- gold
- Cited By
- 6
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389686696
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4389686696Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics12244995Digital Object Identifier
- Title
-
Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place OperationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-13Full publication date if available
- Authors
-
Guanglei Wu, Bin Niu, Qiancheng LiList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12244995Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/12/24/4995/pdf?version=1702465883Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2079-9292/12/24/4995/pdf?version=1702465883Direct OA link when available
- Concepts
-
Iterative learning control, SCARA, Control theory (sociology), Robustness (evolution), Adaptive control, Trajectory, Computer science, Robot, Controller (irrigation), Tracking error, Fuzzy control system, Fuzzy logic, Motion control, Artificial intelligence, Control (management), Physics, Biology, Gene, Chemistry, Astronomy, Biochemistry, AgronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_year | 2023 |
| referenced_works | https://openalex.org/W4229502812, https://openalex.org/W6827854873, https://openalex.org/W4240172577, https://openalex.org/W2013553076, https://openalex.org/W2059163735, https://openalex.org/W2116243329, https://openalex.org/W2106518344, https://openalex.org/W2555059385, https://openalex.org/W2089709206, https://openalex.org/W1982117611, https://openalex.org/W3110402551, https://openalex.org/W4386028736, https://openalex.org/W2161085359, https://openalex.org/W2893707157, https://openalex.org/W1977845698, https://openalex.org/W2995318094, https://openalex.org/W4319004623, https://openalex.org/W2606637440, https://openalex.org/W1506185406, https://openalex.org/W4384472293, https://openalex.org/W6648848944, https://openalex.org/W6758454777, https://openalex.org/W2383543835, https://openalex.org/W2555144699, https://openalex.org/W2979766764, https://openalex.org/W2972339324, https://openalex.org/W3154005288, https://openalex.org/W4380881533, https://openalex.org/W2313706817, https://openalex.org/W4285127453, https://openalex.org/W4212771256, https://openalex.org/W6626435639, https://openalex.org/W3100509213, https://openalex.org/W3147771082, https://openalex.org/W1994197355, https://openalex.org/W1016532093, https://openalex.org/W2911920499 |
| referenced_works_count | 37 |
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| abstract_inverted_index.The | 100, 184, 219 |
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| abstract_inverted_index.which | 61, 207 |
| abstract_inverted_index.39.8%, | 161 |
| abstract_inverted_index.45.8%, | 158 |
| abstract_inverted_index.56.5%, | 157 |
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| abstract_inverted_index.observing | 120 |
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| abstract_inverted_index.0.7×10−4 | 181 |
| abstract_inverted_index.algorithms. | 140 |
| abstract_inverted_index.generalized | 225 |
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| abstract_inverted_index.root-mean-squared | 187 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5076036468 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I27357992 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.8123463 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |