High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle
In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematic...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tanveer, Ahsan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy - Chang, Guanru ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:266 ; year:2022 ; day:15 ; month:12 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.oceaneng.2022.112836 |
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| 245 | 1 | 0 | |a High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle |
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| 520 | |a In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. | ||
| 520 | |a In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. | ||
| 650 | 7 | |a Robust control |2 Elsevier | |
| 650 | 7 | |a micro-ROV |2 Elsevier | |
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| 650 | 7 | |a Marine predators algorithm (MPA) |2 Elsevier | |
| 650 | 7 | |a ROV modelling |2 Elsevier | |
| 650 | 7 | |a PI |2 Elsevier | |
| 650 | 7 | |a Underwater vehicle |2 Elsevier | |
| 650 | 7 | |a Linear quadratic regulator (LQR) |2 Elsevier | |
| 700 | 1 | |a Ahmad, Sarvat M. |4 oth | |
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10.1016/j.oceaneng.2022.112836 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001991.pica (DE-627)ELV059744227 (ELSEVIER)S0029-8018(22)02119-9 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Tanveer, Ahsan verfasserin aut High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. Robust control Elsevier micro-ROV Elsevier Genetic algorithm (GA) Elsevier Linear system identification Elsevier External disturbance Elsevier Marine predators algorithm (MPA) Elsevier ROV modelling Elsevier PI Elsevier Underwater vehicle Elsevier Linear quadratic regulator (LQR) Elsevier Ahmad, Sarvat M. oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.112836 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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10.1016/j.oceaneng.2022.112836 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001991.pica (DE-627)ELV059744227 (ELSEVIER)S0029-8018(22)02119-9 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Tanveer, Ahsan verfasserin aut High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. Robust control Elsevier micro-ROV Elsevier Genetic algorithm (GA) Elsevier Linear system identification Elsevier External disturbance Elsevier Marine predators algorithm (MPA) Elsevier ROV modelling Elsevier PI Elsevier Underwater vehicle Elsevier Linear quadratic regulator (LQR) Elsevier Ahmad, Sarvat M. oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.112836 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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10.1016/j.oceaneng.2022.112836 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001991.pica (DE-627)ELV059744227 (ELSEVIER)S0029-8018(22)02119-9 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Tanveer, Ahsan verfasserin aut High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. Robust control Elsevier micro-ROV Elsevier Genetic algorithm (GA) Elsevier Linear system identification Elsevier External disturbance Elsevier Marine predators algorithm (MPA) Elsevier ROV modelling Elsevier PI Elsevier Underwater vehicle Elsevier Linear quadratic regulator (LQR) Elsevier Ahmad, Sarvat M. oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.112836 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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10.1016/j.oceaneng.2022.112836 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001991.pica (DE-627)ELV059744227 (ELSEVIER)S0029-8018(22)02119-9 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Tanveer, Ahsan verfasserin aut High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. Robust control Elsevier micro-ROV Elsevier Genetic algorithm (GA) Elsevier Linear system identification Elsevier External disturbance Elsevier Marine predators algorithm (MPA) Elsevier ROV modelling Elsevier PI Elsevier Underwater vehicle Elsevier Linear quadratic regulator (LQR) Elsevier Ahmad, Sarvat M. oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.112836 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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10.1016/j.oceaneng.2022.112836 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001991.pica (DE-627)ELV059744227 (ELSEVIER)S0029-8018(22)02119-9 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Tanveer, Ahsan verfasserin aut High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. Robust control Elsevier micro-ROV Elsevier Genetic algorithm (GA) Elsevier Linear system identification Elsevier External disturbance Elsevier Marine predators algorithm (MPA) Elsevier ROV modelling Elsevier PI Elsevier Underwater vehicle Elsevier Linear quadratic regulator (LQR) Elsevier Ahmad, Sarvat M. oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.112836 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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Enthalten in Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy Amsterdam [u.a.] volume:266 year:2022 day:15 month:12 pages:0 |
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high fidelity modelling and ga optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle |
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High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle |
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In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. |
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In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. |
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In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. |
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