PurposeTo determine the impact of noise on heart rate variability (HRV) in men, with a focus on the noise type rather than on noise intensity.Materials and MethodsForty college-going male volunteers were enrolled in this study and were randomly divided into four groups according to the type of noise they were exposed to: background, traffic, speech, or mixed (traffic and speech) noise. All groups except the background group (35 dB) were exposed to 45 dB sound pressure levels. We collected data on age, smoking status, alcohol consumption, and disease status from responses to self-reported questionnaires and medical examinations. We also measured HRV parameters and blood pressure levels before and after exposure to noise. The HRV parameters were evaluated while patients remained seated for 5 minutes, and frequency and time domain analyses were then performed.ResultsAfter noise exposure, only the speech noise group showed a reduced low frequency (LF) value, reflecting the activity of both the sympathetic and parasympathetic nervous systems. The low-to-high frequency (LF/HF) ratio, which reflected the activity of the autonomic nervous system (ANS), became more stable, decreasing from 5.21 to 1.37; however, this change was not statistically significant.ConclusionThese results indicate that 45 dB(A) of noise, 10 dB(A) higher than background noise, affects the ANS. Additionally, the impact on HRV activity might differ according to the noise quality. Further studies will be required to ascertain the role of noise type.
This paper presents a sliding mode control method for wheeled mobile robots. Because of the nonlinear and nonholonomic properties, it is difficult to establish an appropriate model of the mobile robot system for trajectory tracking. A robust control law which is called sliding mode control is proposed for asymptotically stabilizing the mobile robot to a desired trajectory. The posture of the mobile robot (including the position and heading direction) is presented and the kinematics equations are established in the two-dimensional coordinates. According to the kinematics equations, the controller is designed to find an acceptable control law so that the tracking error will approximate 0 as the time approaches infinity with an initial error. The RFID sensor space is used to estimate the real posture of the mobile robot. Simulation and experiment demonstrate the efficacy of the proposed system for robust tracking of mobile robots.
This paper presents a dynamic model-based control scheme for the balancing and velocity control of a unicycle robot. Unicycle robot motion consists of a pitch that is controlled by an in-wheel motor and a roll that is controlled by a reaction wheel pendulum. The unicycle robot lacks an actuator for yaw-axis control, which makes the derivation of the dynamics relatively simple even though it may limit the motion control. The Euler-Lagrange equation is applied to derive the dynamic equations of the unicycle robot to implement dynamic speed control. To achieve real-time speed control, a sliding mode control and a nonzero set-point LQ regulator (LQR) are utilized to guarantee stability while maintaining the desired speed-tracking performance. In the roll controller, a sigmoid-function-based sliding mode controller has been adopted to minimize switching-function chattering. An LQR controller has been implemented for pitch control to drive the unicycle robot to follow the desired velocity trajectory in real time using the state variables of pitch angle, angular velocity, wheel angle, and angular velocity. The control performance of the two control systems using a single dynamic model has been experimentally demonstrated.
Precise tracking positioning performance in the presence of both the deadzone and friction of a robot manipulator actuator is difficult to achieve by traditional control methodology without proper nonlinear compensation schemes. In this paper, we present a dynamic surface sliding mode control scheme combined with an adaptive fuzzy system, state observer, and parameter estimator to estimate the uncertainty, friction, and deadzone nonlinearities of a robot manipulator system. We design a dynamic surface sliding mode basic controller by systematic recursive design steps that yields several adaptive laws for the compensation of nonlinear friction, deadzone, and other unknown nonlinear dynamics. The boundedness and convergence of this closed-loop system are guaranteed by the Lyapunov stability theorem. Experiments on the Scorbot robot manipulator demonstrate the validity and effectiveness of the proposed control scheme.
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