Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm
Aiming to improve the performance of motion for autonomous underwater vehicle (AUV), a fractional-order PID strategy is proposed. It is a more generalized form for the conventional integer-order PID controller, keeping its simplicity and utilizing the generalized derivative and integral control acti...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8817916/ |
| _version_ | 1823926226915426304 |
|---|---|
| author | Junhe Wan Bo He Dianrui Wang Tianhong Yan Yue Shen |
| author_facet | Junhe Wan Bo He Dianrui Wang Tianhong Yan Yue Shen |
| author_sort | Junhe Wan |
| collection | DOAJ |
| description | Aiming to improve the performance of motion for autonomous underwater vehicle (AUV), a fractional-order PID strategy is proposed. It is a more generalized form for the conventional integer-order PID controller, keeping its simplicity and utilizing the generalized derivative and integral control actions. The fractional-order PID controller has been successfully applied to heading control, diving control and path-following system of AUV on sea trial. In addition, the fractional-order closed-loop system has proven to be stable. By comparing simulations and experiments, the satisfactory performance, such as overshoot, settling time and steady-state error, has been achieved. The cloud-model-based quantum genetic algorithm (CQGA) is employed to tune coefficients of fractional-order PID controller. The quantum bits and quantum superposition states avoid the pressure of selection and maintain the diversity of population in chromosome coding. Due to the randomness and stability tendency of cloud droplets, the cloud crossover operator and the cloud mutation operator can effectively overcome the shortcomings of premature and slow searching speed. Numerical simulations show that the CQGA is more efficient to find the optimal coefficients of fractional-order PID controller than GA. |
| first_indexed | 2024-12-16T20:21:00Z |
| format | Article |
| id | doaj.art-4bd127133ad2461a853a467b627797f4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T20:21:00Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4bd127133ad2461a853a467b627797f42022-12-21T22:17:48ZengIEEEIEEE Access2169-35362019-01-01712482812484310.1109/ACCESS.2019.29379788817916Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic AlgorithmJunhe Wan0https://orcid.org/0000-0003-0206-2742Bo He1Dianrui Wang2Tianhong Yan3Yue Shen4School of Information Science and Engineering, Ocean University of China, Qingdao, ChinaSchool of Information Science and Engineering, Ocean University of China, Qingdao, ChinaSchool of Information Science and Engineering, Ocean University of China, Qingdao, ChinaSchool of Mechanical Electrical Engineering, China Jiliang University, Hangzhou, ChinaSchool of Information Science and Engineering, Ocean University of China, Qingdao, ChinaAiming to improve the performance of motion for autonomous underwater vehicle (AUV), a fractional-order PID strategy is proposed. It is a more generalized form for the conventional integer-order PID controller, keeping its simplicity and utilizing the generalized derivative and integral control actions. The fractional-order PID controller has been successfully applied to heading control, diving control and path-following system of AUV on sea trial. In addition, the fractional-order closed-loop system has proven to be stable. By comparing simulations and experiments, the satisfactory performance, such as overshoot, settling time and steady-state error, has been achieved. The cloud-model-based quantum genetic algorithm (CQGA) is employed to tune coefficients of fractional-order PID controller. The quantum bits and quantum superposition states avoid the pressure of selection and maintain the diversity of population in chromosome coding. Due to the randomness and stability tendency of cloud droplets, the cloud crossover operator and the cloud mutation operator can effectively overcome the shortcomings of premature and slow searching speed. Numerical simulations show that the CQGA is more efficient to find the optimal coefficients of fractional-order PID controller than GA.https://ieeexplore.ieee.org/document/8817916/Fractional-order PIDAUVcloud-model-based quantum genetic algorithm (CQGA)steady error |
| spellingShingle | Junhe Wan Bo He Dianrui Wang Tianhong Yan Yue Shen Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm IEEE Access Fractional-order PID AUV cloud-model-based quantum genetic algorithm (CQGA) steady error |
| title | Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm |
| title_full | Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm |
| title_fullStr | Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm |
| title_full_unstemmed | Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm |
| title_short | Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm |
| title_sort | fractional order pid motion control for auv using cloud model based quantum genetic algorithm |
| topic | Fractional-order PID AUV cloud-model-based quantum genetic algorithm (CQGA) steady error |
| url | https://ieeexplore.ieee.org/document/8817916/ |
| work_keys_str_mv | AT junhewan fractionalorderpidmotioncontrolforauvusingcloudmodelbasedquantumgeneticalgorithm AT bohe fractionalorderpidmotioncontrolforauvusingcloudmodelbasedquantumgeneticalgorithm AT dianruiwang fractionalorderpidmotioncontrolforauvusingcloudmodelbasedquantumgeneticalgorithm AT tianhongyan fractionalorderpidmotioncontrolforauvusingcloudmodelbasedquantumgeneticalgorithm AT yueshen fractionalorderpidmotioncontrolforauvusingcloudmodelbasedquantumgeneticalgorithm |