An original sliding mode controller is designed, based on an existing mathematical model for response control of the human vestibular system. The human vestibular system is located in the inner ear and significantly contributes to the functions of detecting head motion, maintaining balance and posture, and realizing gaze stabilization. The vestibular system sends signals to the brain to tell it how the head and body are moving, and the brain reacts by changing eye position accordingly. The nonlinearities of the vestibular system are not completely understood. The biggest nonlinearity is the nystagmus, a bouncing of the eyes to compensate for quick head movement. Another nonlinearity is that the quick phase does not start until head movement reaches a certain frequency. Considering these nonlinearities as well as the uncertainties of the system, sliding mode control a good choice for controlling the system. Several mathematical models of the human vestibular system are considered for use in the control design. The best model of those considered is chosen based on the models’ consideration of nonlinearities and their levels of complexity. The mathematical model used in this paper is a nonlinear transfer function. The output is controlled with a robust sliding mode controller. Results demonstrate the need to increase control parameters as frequency of the sinusoidal input increases to minimize overshoot error. However, since the human head cannot tolerate an infinitely large frequency input, control parameters also will necessarily be limited. Therefore, results show that the designed sliding mode robust controller is an effective mechanism for controlling the mathematical model of the human vestibular system.
- Aerospace Division
Sliding Mode Control of the Human Vestibular System
McCarty, R, Mahmoodi, SN, & Williams, K. "Sliding Mode Control of the Human Vestibular System." Proceedings of the ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Structural Health Monitoring. Stone Mountain, Georgia, USA. September 19–21, 2012. pp. 383-390. ASME. https://doi.org/10.1115/SMASIS2012-7988
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