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Robust composite nonlinear feedback for nonlinear Steer-by-Wire vehicle’s Yaw control Sarah 'Atifah Saruchi; Mohd Hatta Mohammed Ariff; Hairi Zamzuri; Noraishikin Zulkarnain; Mohd Hanif Che Hasan; Sheikh Muhammad Hafiz Fahami
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Yaw control is a part of an Active Front Steering (AFS) system, which is used to improve vehicle manoeuvrability. Previously, it has been reported that the yaw rate tracking performance of a linear Steer-by-Wire (SBW) vehicle equipped with a Composite Nonlinear Feedback (CNF) controller and a Disturbance Observer (DOB) is robust with respect to side wind disturbance effects. This paper presents further investigation regarding the robustness of the combination between a CNF and a DOB in a nonlinear environment through a developed 7-DOF nonlinear SBW vehicle. Moreover, in contrast to previous studies, this paper also contributes in presenting the validation works of the proposed control system in a real-time situation using a Hardware-in-Loop (HIL) platform. Simulation and validation results show that the CNF and DOB managed to reduce the influence of the side wind disturbance in nonlinearities.

Robust composite nonlinear feedback for nonlinear Steer-by-Wire vehicle’s Yaw control Sarah 'Atifah Saruchi; Mohd Hatta Mohammed Ariff; Hairi Zamzuri; Noraishikin Zulkarnain; Mohd Hanif Che Hasan; Sheikh Muhammad Hafiz Fahami
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Yaw control is a part of an Active Front Steering (AFS) system, which is used to improve vehicle manoeuvrability. Previously, it has been reported that the yaw rate tracking performance of a linear Steer-by-Wire (SBW) vehicle equipped with a Composite Nonlinear Feedback (CNF) controller and a Disturbance Observer (DOB) is robust with respect to side wind disturbance effects. This paper presents further investigation regarding the robustness of the combination between a CNF and a DOB in a nonlinear environment through a developed 7-DOF nonlinear SBW vehicle. Moreover, in contrast to previous studies, this paper also contributes in presenting the validation works of the proposed control system in a real-time situation using a Hardware-in-Loop (HIL) platform. Simulation and validation results show that the CNF and DOB managed to reduce the influence of the side wind disturbance in nonlinearities.

Robust composite nonlinear feedback for nonlinear Steer-by-Wire vehicle’s Yaw control Sarah 'Atifah Saruchi; Mohd Hatta Mohammed Ariff; Hairi Zamzuri; Noraishikin Zulkarnain; Mohd Hanif Che Hasan; Sheikh Muhammad Hafiz Fahami
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Yaw control is a part of an Active Front Steering (AFS) system, which is used to improve vehicle manoeuvrability. Previously, it has been reported that the yaw rate tracking performance of a linear Steer-by-Wire (SBW) vehicle equipped with a Composite Nonlinear Feedback (CNF) controller and a Disturbance Observer (DOB) is robust with respect to side wind disturbance effects. This paper presents further investigation regarding the robustness of the combination between a CNF and a DOB in a nonlinear environment through a developed 7-DOF nonlinear SBW vehicle. Moreover, in contrast to previous studies, this paper also contributes in presenting the validation works of the proposed control system in a real-time situation using a Hardware-in-Loop (HIL) platform. Simulation and validation results show that the CNF and DOB managed to reduce the influence of the side wind disturbance in nonlinearities.

Radial basis function neural network for head roll prediction modelling in a motion sickness study Sarah ‘Atifah Saruchi; Mohd Hatta Mohammed Ariff; Mohd Ibrahim Shapiai; Nurhaffizah Hassan; Nurbaiti Wahid; Noor Jannah Zakaria; Mohd Azizi Abdul Rahman; Hairi Zamzuri
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1637-1644

Motion Sickness (MS) is the result of uneasy feelings that occurs when travelling. In MS mitigation studies, it is necessary to investigate and measure the occupant’s Motion Sickness Incidence (MSI) for analysis purposes. One way to mathematically calculate the MSI is by using a 6-DOF Subjective Vertical Conflict (SVC) model. This model utilises the information of the vehicle lateral acceleration and the occupant’s head roll angle to determine the MSI. The data of the lateral acceleration can be obtained by using a sensor. However, it is impractical to use a sensor to acquire the occupant’s head roll response. Therefore, this study presents the occupant’s head roll prediction model by using the Radial Basis Function Neural Network (RBFNN) method to estimate the actual head roll responses. The prediction model is modelled based on the correlation between lateral acceleration and head roll angle during curve driving. Experiments have been conducted to collect real naturalistic data for modelling purposes. The results show that the predicted responses from the model are similar with the real responses from the experiment. In future, it is expected that the prediction model will be useful in measuring the occupant’s MSI level by providing the estimated head roll responses.

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