Fuzzy-based optimization and linear feedback fluid actuators for soft handheld robots

Vartika Kulshrestha; S. Balu Mahandiran; Prasad Yadav Kurikyala; A. V. G. A. Marthanda; Manikandan Ganesan; Velpula Sampath

doi:10.53730/ijhs.v6nS3.8732

Authors

Vartika Kulshrestha Assistant Professor, Department of Computer Science & Engineering, Alliance University, Bangalore, Karnataka, India
S. Balu Mahandiran Assistant Professor, Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Kuniyamuthur, Coimbatore, India
Prasad Yadav Kurikyala Lecturer in Electrical Engineering Department, University of Technology and Applied Sciences-Ibri Engineering department, PO Box 466, Postal Code 516, Ibri, Sultanate of Oman
A. V. G. A. Marthanda Associate professor, Department of EEE, Laki Reddy Bali Reddy college of Engineering, Mylavaram, (Permanently affiliated to JNTU Kakinada), Andhra Pradesh
Manikandan Ganesan Lecturer, Department of Electromechanical Engineering, Faculty of Manufacturing, Institute of Technology, Hawassa University, Hawassa, Ethiopia
Velpula Sampath Assistant Professor, Department of Mechanical Engineering, Telangana, India

Keywords:

optimized hydraulic, muscle activator, artificial intelligence, fuzzy system, neural networks, linear fluid actuators

Abstract

Because these were elastic & inexpensive, Hydraulic Muscle Activator (HMA) has a lot of promise in portable and responsive rehabilitative equipment. The transducers' changing and continuous response, on the other hand, create modeling and management problems that are hard to grasp. Our study offers a novel portable ankle rehabilitative robotic, that globes only of its type, which is powered with Optimized HMAs (OHMA) run tandem. The goal of this paper is to provide an adaptive regulator that will help OHMA-driven gadgets conquer their problems. To properly anticipate that performance for OHMA, fuzzy feedback regulate operator is developed. To find the best combination input settings for imprecise controllers, a Genetic Algorithm (GA) Based is used. The propoed OHMA-driven gait training robots have been evaluated, and the iteration controllers are effective in tracing the complicated connection between length, velocity, and tension of the OHMA with great precision. Whenever provided several intended trajectories, empirical findings demonstrate that the microcontroller can follow them quite well.

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