ArticleScientific reports2026
Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adaptive control of lower-limb rehabilitation robots is challenging due to the nonlinear dynamics of coupled joints, patient-specific uncertainties, and external disturbances during human–robot interaction. This study presents a hybrid quantum-inspired adaptive intelligent fuzzy control framework for a 3-DOF lower-limb rehabilitation robot. The quantum approximate optimization algorithm (QAOA) is used to tune fuzzy membership function parameters online, while the fuzzy logic controller uses joint position error and its derivative in a closed-loop configuration. Triangular membership functions are adjusted in real time to minimize the integral of squared error. The proposed controller is evaluated through simulations on a nonlinear dynamic model of the robot, including coupled joint interactions and typical disturbances. The results indicate the improvements in trajectory tracking accuracy, disturbance rejection, and energy dissipation compared to conventional fuzzy logic controllers and recent adaptive or reinforcement-learning-based methods. An energy-based analysis combined with Lyapunov stability assessment to confirm the enhanced closed-loop stability, showing faster energy dissipation for the QAOA-optimized fuzzy controller. The results ensure that integrating quantum optimization with fuzzy control can improve robustness and accuracy of rehabilitation robots, effectively handling nonlinearities and patient-specific uncertainties. The study employed Hardware-in-the-Loop testing to prove that the proposed controller achieves its intended performance during actual operation although the system requires ongoing monitoring for any performance-related issues. Results demonstrate that the QAOA-optimized fuzzy controller reduces the integral squared error (ISE) by approximately 96–99%, settling time by 62–63%, and limiting peak overshoot by nearly 75–80% across all considered test systems relative to previously reported controllers.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.