Heart Rate Modeling and SMC Simulation During Tredmill Exercise

Kim, Hwan Young; Lee, Min Cheol

doi:10.1109/ict-robot.2018.8549860

Cited by 1 publication

(6 citation statements)

References 8 publications

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“…As can be observed in (17), the sliding surface dynamics are affected by the perturbation (when σ = 0). Therefore, the system stability is not guaranteed during the reaching phase.…”

Section: Sliding Mode Controlmentioning

confidence: 79%

“…The sliding surface of SMC a has large fluctuation (dotted red line), as shown in Figure 8, because of the presence of the disturbance in (17). By comparison, the fluctuation in ISMC is almost negligible (solid blue line) compared with that of the SMC.…”

Section: Fluctuating Sliding Surfacementioning

confidence: 95%

“…The SMC output has a fluctuation, whereas ISMC output has a smooth performance. The tracking output of SMC has a fluctuation because the actual sliding surface dynamics are affected by the perturbation (17); by comparison, the output tracking of ISMC is smooth compared with that of conventional SMC because the actual sliding surface of ISMC ( 29) is free of perturbation effects.…”

Section: Better Trajectory Trackingmentioning

confidence: 99%

“…The human heart is a nonlinear system and the design of a controller to track the reference heart rate during treadmill exercise is a challenging task. Thus, researchers have proposed different control algorithms to achieve this task [17][18][19][20]. Su et al [18] proposed a fuzzy neural network to control the treadmill speed during exercise.…”

Section: Introductionmentioning

confidence: 99%

“…It is not easy to implement the above-mentioned control techniques because of their computational complexities. Kim et al [17] presented linear control (PI) for human heart rate control during exercise. It is easy to implement PI control but the heart rate tracking error is large.…”

Section: Introductionmentioning

confidence: 99%

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Robust Control Design of a Human Heart Rate System for Cardiac Rehabilitation Exercise

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Automatic, precise, and accurate heart rate control during treadmill exercise is an interesting topic among researchers. The human heart is a highly nonlinear system. Conventional control techniques are not sufficient and it is difficult to accurately model the human heart. Two different robust controllers were designed for this nonlinear system. Firstly, sliding mode control (SMC) was implemented; SMC is robust against parametric uncertainties and external disturbance but its robustness is not guaranteed during the reaching phase, especially in heart rate control, and implementation of SMC requires the linear parameters of the system (human heart rate model). In this research, the signal compression method (SCM) was used for approximately linearized modeling of the human heart rate. The extraction of the human heart rate model using SCM requires experiment and computation. Furthermore, it was observed in this research that SCM is not a precise method. Therefore, integral sliding mode control (ISMC) was designed and implemented to overcome these difficulties. By introducing an auxiliary sliding surface, the reaching phase and effect of the perturbation on an actual sliding surface were eliminated; furthermore, implementation of ISMC does not require the linear parameters of the system. Simulations were performed in MATLAB/Simulink and experiments were conducted in a hospital. Six clinical subjects participated in this experiment. Both forms of control logic were implemented during the desired heart rate tracking test. Results showed that the desired heart rate tracking of ISMC is better than that of SMC. The tracking error of ISMC is smaller than that of SMC. However, ISMC control output has chattering, which needs to be reduced.

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“…As can be observed in (17), the sliding surface dynamics are affected by the perturbation (when σ = 0). Therefore, the system stability is not guaranteed during the reaching phase.…”

Section: Sliding Mode Controlmentioning

confidence: 79%

Section: Fluctuating Sliding Surfacementioning

confidence: 95%

Section: Better Trajectory Trackingmentioning

confidence: 99%