Fuzzy Dynamic Modeling for Accurate Control of Uncertain Mechanical Systems
Mechanical systems become more complex, achieving precise control becomes increasingly challenging due to uncertainty. This study presents a fuzzy dynamics-based strategy for precise control of uncertain mechanical systems and investigates the use of robust control theory to assess system performanc...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10353917/ |
| _version_ | 1797376280723718144 |
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| author | Chendi Shi Bao Liu |
| author_facet | Chendi Shi Bao Liu |
| author_sort | Chendi Shi |
| collection | DOAJ |
| description | Mechanical systems become more complex, achieving precise control becomes increasingly challenging due to uncertainty. This study presents a fuzzy dynamics-based strategy for precise control of uncertain mechanical systems and investigates the use of robust control theory to assess system performance and stability under constraints. Fuzzy theory enables uncertainties to be addressed, resulting in a more precise description of system behaviour. The study findings demonstrate that utilising the constraint invariant dynamics analysis method led to a decrease in control input amplitude, resulting in an average total input U reduction of approximately 60 volts and improving system stability. The constraint invariant dynamics analysis method led to an average reduction of 40 volts in u1 amplitude and an average position error of 1 mm under motor control. The experimentation undertaken on the permanent magnet synchronous motor angular trajectory exhibits that each test was successful in following the anticipated path. The average angular discrepancies between experiments A, B, C, and D were 0.5, 1, 0.3, and 0.8 degrees respectively. The experimental trajectories for A and B occasionally surpassed the upper limit, while C and D remained consistently within the upper and lower bounds. The implementation of the state-dependent control strategy resulted in a 10% reduction in standard deviation of current fluctuation on average, further enhancing the stability and efficiency of the motor system. The research results are expected to provide more stable and efficient control solutions for a wide range of industrial and engineering applications, thereby making a positive contribution to sustainable development and technological progress in society. |
| first_indexed | 2024-03-08T19:36:16Z |
| format | Article |
| id | doaj.art-483d5de1916f41dbb52f39e8154b9811 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:36:16Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-483d5de1916f41dbb52f39e8154b98112023-12-26T00:10:15ZengIEEEIEEE Access2169-35362023-01-011114120514121610.1109/ACCESS.2023.334149810353917Fuzzy Dynamic Modeling for Accurate Control of Uncertain Mechanical SystemsChendi Shi0https://orcid.org/0009-0009-7375-9353Bao Liu1Department of Rural Revitalization Education, Jilin Engineering Vocational College, Siping, ChinaDepartment of Management, Jilin Engineering Vocational College, Siping, ChinaMechanical systems become more complex, achieving precise control becomes increasingly challenging due to uncertainty. This study presents a fuzzy dynamics-based strategy for precise control of uncertain mechanical systems and investigates the use of robust control theory to assess system performance and stability under constraints. Fuzzy theory enables uncertainties to be addressed, resulting in a more precise description of system behaviour. The study findings demonstrate that utilising the constraint invariant dynamics analysis method led to a decrease in control input amplitude, resulting in an average total input U reduction of approximately 60 volts and improving system stability. The constraint invariant dynamics analysis method led to an average reduction of 40 volts in u1 amplitude and an average position error of 1 mm under motor control. The experimentation undertaken on the permanent magnet synchronous motor angular trajectory exhibits that each test was successful in following the anticipated path. The average angular discrepancies between experiments A, B, C, and D were 0.5, 1, 0.3, and 0.8 degrees respectively. The experimental trajectories for A and B occasionally surpassed the upper limit, while C and D remained consistently within the upper and lower bounds. The implementation of the state-dependent control strategy resulted in a 10% reduction in standard deviation of current fluctuation on average, further enhancing the stability and efficiency of the motor system. The research results are expected to provide more stable and efficient control solutions for a wide range of industrial and engineering applications, thereby making a positive contribution to sustainable development and technological progress in society.https://ieeexplore.ieee.org/document/10353917/Control strategiesuncertain mechanical systemsCIDA methodfuzzy dynamicsHORC control |
| spellingShingle | Chendi Shi Bao Liu Fuzzy Dynamic Modeling for Accurate Control of Uncertain Mechanical Systems IEEE Access Control strategies uncertain mechanical systems CIDA method fuzzy dynamics HORC control |
| title | Fuzzy Dynamic Modeling for Accurate Control of Uncertain Mechanical Systems |
| title_full | Fuzzy Dynamic Modeling for Accurate Control of Uncertain Mechanical Systems |
| title_fullStr | Fuzzy Dynamic Modeling for Accurate Control of Uncertain Mechanical Systems |
| title_full_unstemmed | Fuzzy Dynamic Modeling for Accurate Control of Uncertain Mechanical Systems |
| title_short | Fuzzy Dynamic Modeling for Accurate Control of Uncertain Mechanical Systems |
| title_sort | fuzzy dynamic modeling for accurate control of uncertain mechanical systems |
| topic | Control strategies uncertain mechanical systems CIDA method fuzzy dynamics HORC control |
| url | https://ieeexplore.ieee.org/document/10353917/ |
| work_keys_str_mv | AT chendishi fuzzydynamicmodelingforaccuratecontrolofuncertainmechanicalsystems AT baoliu fuzzydynamicmodelingforaccuratecontrolofuncertainmechanicalsystems |