scholarly journals A fuzzy control system for fitness service based on genetic algorithm during COVID-19 pandemic

2020 ◽  
Vol 39 (6) ◽  
pp. 8805-8812
Author(s):  
Zhihui He ◽  
Xiaofeng Li
Keyword(s):  
Genetic Algorithm ◽  
Heart Rate ◽  
Control System ◽  
Fuzzy Control ◽  
Aerobic Exercise ◽  
Exercise Intensity ◽  
Fuzzy Membership Function ◽  
Fuzzy Control System ◽  
Intensity Control ◽  
Epidemic Period

During the COVID-19 epidemic period, it is essential to strengthen physical exercise and improve the health of the whole people. In this paper, based on genetic algorithm, a fuzzy control system is proposed to dynamically adjust the exercise ability of the bodybuilders under the comprehensive consideration of parameters. Through experiments and data processing, the system obtains bioelectric information related to heart rate, heart rate variability and muscle fatigue of the fitness people in the three states of not fatigue, moderate fatigue and extreme fatigue, establishes fuzzy membership function, and thus establishes personalized fitness information feedback control strategy to maintain moderate fitness intensity. By narrowing the gap between the predicted RPE value based on objective information and the measured RPE, the method provides a unified subjective and objective exercise intensity for the bodybuilders, effectively expands the time of aerobic exercise, and enhances the effect of aerobic exercise. In addition, in order to expand the scope of application of the exercise intensity control model, the service-oriented transformation is carried out to enable it to provide fitness content combinations of interest to fitness practitioners and instructors.

Stability Analysis of Nonlinear Dynamic Systems by Nonlinear Takagi–Sugeno–Kang Fuzzy Systems

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Zahra Namadchian ◽  
Assef Zare ◽  
Ali Namadchian
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Fuzzy Control ◽  
Limit Cycle ◽  
Phase Margin ◽  
Fuzzy Control System ◽  
Gain Margin ◽  
The Stability ◽  
Takagi Sugeno ◽  
System 1

This paper proposes a systematic procedure to address the limit cycle prediction of a Nonlinear Takagi–Sugeno–Kang (NTSK) fuzzy control system with adjustable parameters. NTSK fuzzy can be linearized by describing function method. The stability of the equivalent linearized system is then analyzed using the stability equations and the parameter plane method. After that the gain–phase margin (PM) tester has been added, then gain margin (GM) and phase margin for limit cycle are analyzed. Using NTSK fuzzy control system can help to have fewer rules. In order to analyze the stability with the same technique of stability analysis, the results of NTSK fuzzy control system will be compared with Dynamic fuzzy control system [1]. Computer simulations show differences between both systems.

scholarly journals Absolutely Stable of Takagi-Sugeno Fuzzy Control System by Using Popov’s Criterion

2012 ◽  
Vol 03 (10) ◽  
pp. 1124-1127 ◽  
Author(s):  
Azhdar Soleymanpour Bakefayat ◽  
Aghileh Heydari
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Control System ◽  
Takagi Sugeno
Sign in / Sign up

Export Citation Format

Share Document