• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.257-263
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• 2022

Active stabilizers use signals such as steering angle, yaw rate, and lateral acceleration to vary the roll stiffness of the front and rear suspension depending on the vehicle's driving conditions, and are attracting attention as RSC (Roll Stability Control) system that suppresses roll when turning and improves ride comfort when going straight. Various studies have been conducted in relation to active stabilizer bars and RSC systems. However, accurate modeling of passive stabilizer model and active stabilizer model and vehicle dynamics analysis result verification are insufficient, and performance result analysis related to vehicle roll angle estimation and electric motor control is insufficient. Therefore, in this study, an accurate vehicle dynamics model was constructed by measuring the passive/active stabilizer bar model and component parameters. Based on this, the analysis result with high reliability was derived by comparing the roll angle estimation algorithm based on the lateral acceleration and suspension of the vehicle with the actual vehicle driving test result. In addition, it was intended to accurately analyze the motor torque characteristics and roll reduction effects of the electric motor-driven RSC system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.211-214
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• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\sub$x/, B$\sub$y/, B$\sub$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, learning itself, and the adequacy of the design controller. Also, the performance of the controller can be verified through simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.21-28
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• 2010

This paper describes the mathematical model and controller design for Manta-type Unmanned Underwater Test Vehicle (MUUTV) with 6 DOF nonlinear dynamic equations. The mathematical model contains hydrodynamic forces and moments expressed in terms of a set of hydrodynamic coefficients which were obtained through the PMM (Planar Motion Mechanism) test. Based on the 6 DOF dynamic equations, numerical simulations have been performed to analyze the dynamic performances of the MUUTV. In addition, using the mathematical model PID and sliding mode controller are constructed for the diving and steering maneuver. Simulation results show that the control performances of the MUUTV and compared with these of NPS (Naval Postgraduate School) AUV II.