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qaoa flc  (MathWorks Inc)


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    Structured Review

    MathWorks Inc qaoa flc
    The overall structure of the <t>proposed</t> <t>QAOA-FLC</t> control system.
    Qaoa Flc, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1914 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/qaoa flc/product/MathWorks Inc
    Average 96 stars, based on 1914 article reviews
    qaoa flc - by Bioz Stars, 2026-04
    96/100 stars

    Images

    1) Product Images from "Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization"

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    Journal: Scientific Reports

    doi: 10.1038/s41598-026-41647-4

    The overall structure of the proposed QAOA-FLC control system.
    Figure Legend Snippet: The overall structure of the proposed QAOA-FLC control system.

    Techniques Used: Control

    Comparison of QAOA-FLC and PID performance under baseline, disturbance, and noise conditions.
    Figure Legend Snippet: Comparison of QAOA-FLC and PID performance under baseline, disturbance, and noise conditions.

    Techniques Used: Comparison

    Hip joint tracking with fixed and QAOA-FLC under moderate load disturbance RMSE: 0.5179 → 0.0372, Overshoot: 12.6% → 2.9%, transit time: 2.60 → 0.95 s.
    Figure Legend Snippet: Hip joint tracking with fixed and QAOA-FLC under moderate load disturbance RMSE: 0.5179 → 0.0372, Overshoot: 12.6% → 2.9%, transit time: 2.60 → 0.95 s.

    Techniques Used:

    Knee joint tracking with fixed and QAOA-FLC moderate load disturbance, RMSE: 0.3137 → 0.0187, Overshoot: 8.2% → 1.4%, transit time: 2.10 → 0.80 s.
    Figure Legend Snippet: Knee joint tracking with fixed and QAOA-FLC moderate load disturbance, RMSE: 0.3137 → 0.0187, Overshoot: 8.2% → 1.4%, transit time: 2.10 → 0.80 s.

    Techniques Used:

    Ankle joint tracking with fixed and QAOA-FLC under moderate load disturbance, RMSE: 0.3177 → 0.0223, Overshoot: 9.8% → 2.0%, transit time: 2.35 → 0.90 s.
    Figure Legend Snippet: Ankle joint tracking with fixed and QAOA-FLC under moderate load disturbance, RMSE: 0.3177 → 0.0223, Overshoot: 9.8% → 2.0%, transit time: 2.35 → 0.90 s.

    Techniques Used:

    Hip joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4820 → 0.0415, Overshoot: 11.8% → 3.1%, Transit time: 2.55 → 0.92 s.
    Figure Legend Snippet: Hip joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4820 → 0.0415, Overshoot: 11.8% → 3.1%, Transit time: 2.55 → 0.92 s.

    Techniques Used:

    Knee joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4365 → 0.0398, Overshoot: 10.9% → 2.7%, Transit time: 2.45 → 0.90 s.
    Figure Legend Snippet: Knee joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4365 → 0.0398, Overshoot: 10.9% → 2.7%, Transit time: 2.45 → 0.90 s.

    Techniques Used:

    Ankle joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.3952 → 0.0441, Overshoot: 9.7% → 2.5%, Transit time: 2.40 → 0.88 s.
    Figure Legend Snippet: Ankle joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.3952 → 0.0441, Overshoot: 9.7% → 2.5%, Transit time: 2.40 → 0.88 s.

    Techniques Used:

    Joint tracking performance of QAOA-FLC and standard FLC under 4% sensor noise. Hip RMSE: 0.1729→ 0.0225, Knee RMSE: 0.1144 → 0.0247, Ankle RMSE: 0.0979 → 0.0275.
    Figure Legend Snippet: Joint tracking performance of QAOA-FLC and standard FLC under 4% sensor noise. Hip RMSE: 0.1729→ 0.0225, Knee RMSE: 0.1144 → 0.0247, Ankle RMSE: 0.0979 → 0.0275.

    Techniques Used:

    Joint tracking performance of QAOA-FLC and standard FLC under 40% sensor noise, Hip RMSE: 4.2875→ 0.1605, Knee RMSE: 2.2249 → 0.1857, Ankle RMSE: 1.6203 → 0.2313.
    Figure Legend Snippet: Joint tracking performance of QAOA-FLC and standard FLC under 40% sensor noise, Hip RMSE: 4.2875→ 0.1605, Knee RMSE: 2.2249 → 0.1857, Ankle RMSE: 1.6203 → 0.2313.

    Techniques Used:

    Total system energy of the 3-DOF rehabilitation robot under FLC and QAOA-FLC.
    Figure Legend Snippet: Total system energy of the 3-DOF rehabilitation robot under FLC and QAOA-FLC.

    Techniques Used:



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    qaoa flc  (MathWorks Inc)
    96
    MathWorks Inc qaoa flc
    The overall structure of the <t>proposed</t> <t>QAOA-FLC</t> control system.
    Qaoa Flc, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/qaoa flc/product/MathWorks Inc
    Average 96 stars, based on 1 article reviews
    qaoa flc - by Bioz Stars, 2026-04
    96/100 stars
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    The overall structure of the proposed QAOA-FLC control system.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: The overall structure of the proposed QAOA-FLC control system.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques: Control

    Comparison of QAOA-FLC and PID performance under baseline, disturbance, and noise conditions.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Comparison of QAOA-FLC and PID performance under baseline, disturbance, and noise conditions.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques: Comparison

    Hip joint tracking with fixed and QAOA-FLC under moderate load disturbance RMSE: 0.5179 → 0.0372, Overshoot: 12.6% → 2.9%, transit time: 2.60 → 0.95 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Hip joint tracking with fixed and QAOA-FLC under moderate load disturbance RMSE: 0.5179 → 0.0372, Overshoot: 12.6% → 2.9%, transit time: 2.60 → 0.95 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Knee joint tracking with fixed and QAOA-FLC moderate load disturbance, RMSE: 0.3137 → 0.0187, Overshoot: 8.2% → 1.4%, transit time: 2.10 → 0.80 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Knee joint tracking with fixed and QAOA-FLC moderate load disturbance, RMSE: 0.3137 → 0.0187, Overshoot: 8.2% → 1.4%, transit time: 2.10 → 0.80 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Ankle joint tracking with fixed and QAOA-FLC under moderate load disturbance, RMSE: 0.3177 → 0.0223, Overshoot: 9.8% → 2.0%, transit time: 2.35 → 0.90 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Ankle joint tracking with fixed and QAOA-FLC under moderate load disturbance, RMSE: 0.3177 → 0.0223, Overshoot: 9.8% → 2.0%, transit time: 2.35 → 0.90 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Hip joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4820 → 0.0415, Overshoot: 11.8% → 3.1%, Transit time: 2.55 → 0.92 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Hip joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4820 → 0.0415, Overshoot: 11.8% → 3.1%, Transit time: 2.55 → 0.92 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Knee joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4365 → 0.0398, Overshoot: 10.9% → 2.7%, Transit time: 2.45 → 0.90 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Knee joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4365 → 0.0398, Overshoot: 10.9% → 2.7%, Transit time: 2.45 → 0.90 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Ankle joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.3952 → 0.0441, Overshoot: 9.7% → 2.5%, Transit time: 2.40 → 0.88 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Ankle joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.3952 → 0.0441, Overshoot: 9.7% → 2.5%, Transit time: 2.40 → 0.88 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Joint tracking performance of QAOA-FLC and standard FLC under 4% sensor noise. Hip RMSE: 0.1729→ 0.0225, Knee RMSE: 0.1144 → 0.0247, Ankle RMSE: 0.0979 → 0.0275.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Joint tracking performance of QAOA-FLC and standard FLC under 4% sensor noise. Hip RMSE: 0.1729→ 0.0225, Knee RMSE: 0.1144 → 0.0247, Ankle RMSE: 0.0979 → 0.0275.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Joint tracking performance of QAOA-FLC and standard FLC under 40% sensor noise, Hip RMSE: 4.2875→ 0.1605, Knee RMSE: 2.2249 → 0.1857, Ankle RMSE: 1.6203 → 0.2313.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Joint tracking performance of QAOA-FLC and standard FLC under 40% sensor noise, Hip RMSE: 4.2875→ 0.1605, Knee RMSE: 2.2249 → 0.1857, Ankle RMSE: 1.6203 → 0.2313.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Total system energy of the 3-DOF rehabilitation robot under FLC and QAOA-FLC.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Total system energy of the 3-DOF rehabilitation robot under FLC and QAOA-FLC.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques: