Rehabilitation robotics has developed into an interdisciplinary field which uses mechanical design and control theory and optimization techniques together with information technologies to create better recovery results for people who suffer from motor disabilities. The present review assesses rehabilitation robotics research through engineering application studies which use more than 120 peer-reviewed articles published between 2014 and 2024. The discussion covers four main areas which include control strategies that start from basic PID methods and extend to sophisticated adaptive and intelligent control systems. The study utilizes bio-inspired and metaheuristic optimization methods to enhance system functionality and develop control paths. The system uses cloud and IoT technology to deliver remote medical monitoring services and perform data analysis and create rehabilitation systems which can grow in capacity. The study investigates new human-robot interaction methods which include voice-activated control systems. Existing reviews often address these aspects in isolation, but this work presents an integrated taxonomy and highlights cross-domain synergies that drive innovation in rehabilitation systems. The analysis reveals two research gaps which include researchers who study multimodal biosignal integration with real-time adaptive control and researchers who need to create affordable modular systems for use in resource-limited environments. The findings of this review present engineering-based evidence which shows the requirements for building intelligent accessible and sustainable rehabilitation robots of the future.
International Journal on Technical and Physical Problems of Engineering
An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO). The structure of the controller consists of two models :the modified Elman neural network (MENN) and the feed forward multi-layer Perceptron (MLP). The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. Th
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This paper gives a comprehensive review of PPG’s status, preparation, and mechanisms. Many sorts of PPGs are categorized, for example, millimeter-sized preformed p
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