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Image Search Results


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: