Output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints
This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employ...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Elhaki, Omid [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation - Clarke, C.G.D. ELSEVIER, 2021, international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:154 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.robot.2022.104106 |
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ELV057988609 |
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| 245 | 1 | 0 | |a Output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints |
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| 520 | |a This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. | ||
| 520 | |a This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. | ||
| 650 | 7 | |a Reinforcement learning control |2 Elsevier | |
| 650 | 7 | |a Actuator dynamics |2 Elsevier | |
| 650 | 7 | |a Tractor with n -trailer |2 Elsevier | |
| 650 | 7 | |a Prescribed performance |2 Elsevier | |
| 650 | 7 | |a High-gain observer |2 Elsevier | |
| 650 | 7 | |a Actuator saturation |2 Elsevier | |
| 700 | 1 | |a Shojaei, Khoshnam |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Clarke, C.G.D. ELSEVIER |t Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation |d 2021 |d international journal |g Amsterdam [u.a.] |w (DE-627)ELV00580583X |
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10.1016/j.robot.2022.104106 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001937.pica (DE-627)ELV057988609 (ELSEVIER)S0921-8890(22)00057-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.64 bkl Elhaki, Omid verfasserin aut Output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. Reinforcement learning control Elsevier Actuator dynamics Elsevier Tractor with n -trailer Elsevier Prescribed performance Elsevier High-gain observer Elsevier Actuator saturation Elsevier Shojaei, Khoshnam oth Enthalten in Elsevier Clarke, C.G.D. ELSEVIER Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation 2021 international journal Amsterdam [u.a.] (DE-627)ELV00580583X volume:154 year:2022 pages:0 https://doi.org/10.1016/j.robot.2022.104106 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.64 Radiologie VZ AR 154 2022 0 |
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10.1016/j.robot.2022.104106 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001937.pica (DE-627)ELV057988609 (ELSEVIER)S0921-8890(22)00057-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.64 bkl Elhaki, Omid verfasserin aut Output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. Reinforcement learning control Elsevier Actuator dynamics Elsevier Tractor with n -trailer Elsevier Prescribed performance Elsevier High-gain observer Elsevier Actuator saturation Elsevier Shojaei, Khoshnam oth Enthalten in Elsevier Clarke, C.G.D. ELSEVIER Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation 2021 international journal Amsterdam [u.a.] (DE-627)ELV00580583X volume:154 year:2022 pages:0 https://doi.org/10.1016/j.robot.2022.104106 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.64 Radiologie VZ AR 154 2022 0 |
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10.1016/j.robot.2022.104106 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001937.pica (DE-627)ELV057988609 (ELSEVIER)S0921-8890(22)00057-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.64 bkl Elhaki, Omid verfasserin aut Output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. Reinforcement learning control Elsevier Actuator dynamics Elsevier Tractor with n -trailer Elsevier Prescribed performance Elsevier High-gain observer Elsevier Actuator saturation Elsevier Shojaei, Khoshnam oth Enthalten in Elsevier Clarke, C.G.D. ELSEVIER Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation 2021 international journal Amsterdam [u.a.] (DE-627)ELV00580583X volume:154 year:2022 pages:0 https://doi.org/10.1016/j.robot.2022.104106 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.64 Radiologie VZ AR 154 2022 0 |
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10.1016/j.robot.2022.104106 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001937.pica (DE-627)ELV057988609 (ELSEVIER)S0921-8890(22)00057-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.64 bkl Elhaki, Omid verfasserin aut Output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. Reinforcement learning control Elsevier Actuator dynamics Elsevier Tractor with n -trailer Elsevier Prescribed performance Elsevier High-gain observer Elsevier Actuator saturation Elsevier Shojaei, Khoshnam oth Enthalten in Elsevier Clarke, C.G.D. ELSEVIER Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation 2021 international journal Amsterdam [u.a.] (DE-627)ELV00580583X volume:154 year:2022 pages:0 https://doi.org/10.1016/j.robot.2022.104106 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.64 Radiologie VZ AR 154 2022 0 |
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10.1016/j.robot.2022.104106 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001937.pica (DE-627)ELV057988609 (ELSEVIER)S0921-8890(22)00057-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.64 bkl Elhaki, Omid verfasserin aut Output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. Reinforcement learning control Elsevier Actuator dynamics Elsevier Tractor with n -trailer Elsevier Prescribed performance Elsevier High-gain observer Elsevier Actuator saturation Elsevier Shojaei, Khoshnam oth Enthalten in Elsevier Clarke, C.G.D. ELSEVIER Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation 2021 international journal Amsterdam [u.a.] (DE-627)ELV00580583X volume:154 year:2022 pages:0 https://doi.org/10.1016/j.robot.2022.104106 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.64 Radiologie VZ AR 154 2022 0 |
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English |
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Enthalten in Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation Amsterdam [u.a.] volume:154 year:2022 pages:0 |
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Enthalten in Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation Amsterdam [u.a.] volume:154 year:2022 pages:0 |
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Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation |
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Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation |
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Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation |
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output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints |
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Output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints |
| abstract |
This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. |
| abstractGer |
This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. |
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This paper proposes a novel robust saturated actor–critic multi-layer neural network controller for electrically-driven tractors with n -trailer with unmeasurable linear and angular velocities, uncertain complex dynamics and actuator saturation while guaranteeing a prescribed performance with employing the motor dynamics. The proposed controller consists of four control loops. In the first loop, tracking errors are transformed into constraint errors via prescribed performance bounds. Then, a kinematic controller is designed. In the second loop, an output feedback robust dynamic controller is proposed via multi-layer actor–critic neural networks to approximate model uncertainties, a high-gain observer (HGO) to estimate velocities, and an adaptive robust controller to compensate external dynamic disturbances. Afterwards, a robust actuator controller is designed in third loop by employing multi-layer actor–critic neural networks to deeply diminish unknown nonlinear functions effects, and an adaptive robust controller to handle the bounded actuator disturbances. An auxiliary subsystem is considered in the final loop to reduce the danger of actuator saturation by designing an auxiliary intermediate controller. The stability under the proposed controller is studied by the Lyapunov stability synthesis, and it is proven that tracking errors remain uniformly ultimately bounded. Finally, the validity, reliability, and effectiveness of the proposed reinforcement learning-based controller is shown by means of multiple simulations and some comparisons with a quantitative study. |
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Output-feedback robust saturated actor–critic multi-layer neural network controller for multi-body electrically driven tractors with n -trailer guaranteeing prescribed output constraints |
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