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RISS 인기검색어
- 검색키워드
- 저자 : Fitri Yakub (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Explicit controller of a single truck stability and rollover mitigation
Fitri Yakub,Pauziah Muhammad,Hoong Thiam Toh,M. Sofian Abu Talip,Yasuchika Mori 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.9
This study’s aim was to enhance the maneuverability safety in the coordination of active rear steering and differential braking control for untripped rollover prevention, which performs a panic lane change maneuver to bypass the obstacle encountered in the path. In avoiding rollover accidents, there are several guidance preventions, such as to secure the vehicle from the intention o f the driver and to position the vehicle in the actual lane. A crosswind effect is also found to be a crucial factor since this may cause other accidents. Therefore, there is a need to monitor the driver’s actual path and maintaining the stability of the vehicle along the desired path in order to avoid rollover accidents. We extended the analysis of Yakub and Mori (2015) [1], by suggesting an explicit model of predictive control, which includes an active rear steering and braking control for each wheel. Our main focus was on the general trade-off between rollover prevention and path tracking. The effectiveness of the explicit control model invented for this study was measured and validated by the simulation results for a heavy vehicle proposed in this research.
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Fitri Yakub,Shihao Lee,Yasuchika Mori 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.8
This paper compares the Model predictive control (MPC) and Linear quadratic control (LQC) of heavy vehicles via active front steering for rollover prevention in inclement environments. In both control methods, a Disturbance rejection control (DRC) that negates the effects of wind and road bank acting on the vehicle is designed. Load transfer ratio (LTR) is applied to judge rollover by mitigating the absolute value of LTR as much as possible. We tested and compared two different controllers, i) MPC with DRC and ii) LQC with DRC. Two types of environmental conditions were considered, i) typhoon and ii) typhoon on a bumpy road. The simulation results demonstrate that MPC was more successfully implemented than LQC during LTR suppression. This paper also proposes an MPC for coordination of active rear steering and differential braking control maneuvers to prevent rollover in inclement environments. For a feasible comparison, the LQC controller was designed using the same approach adopted for the MPC controller. Results show the proposed coordinated control yields better performance for rollover prevention than LQC.
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NCTF-EMRAN Control Method for a Two-Mass Rotary Positioning Systems
Mohd Fitri Mohd Yakub,Andika Aji Wijaya 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
In this study, a nominal characteristic trajectory following (NCTF) controller with extended minimal resource allocation network (EMRAN) compensator is introduced for two-mass rotary point-to-point (PTP) positioning systems. Generally, the NCTF controller consists of a nominal characteristic trajectory (NCT) and a compensator. The objective of the compensator is to make the object motion follow the NCT and end at its origin. The NCTF controller is designed based on a simple open-loop experiment of the object. The parameters and an exact model of the plant are not necessary for controller design. This paper presents a new method to improve the conventional NCTF controller in order to increase the positioning performance and robustness of the system by using EMRAN as a compensator. The NCTF-EMRAN controller is evaluated and discussed based on results of simulation. The effect of the design parameters on the robustness of the NCTF-EMRAN controller to inertia variations is evaluated and compared with conventional NCTF controller. It is shown that NCTF-EMRAN controller has a better positioning performance and much more robust than existed NCTF controller.
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Animal Intrusion Detection in Farming Area using YOLOv5 Approach
Normaisharah Mamat,Mohd Fauzi Othman,Fitri Yakub 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Animal intrusion in the farming area causes significant losses in agriculture. It threatens not only the safety of farmers but also contributes to crop damage. Providing effective solutions for human-animals conflict is now one of the most significant challenges all over the world. Therefore, early detection of animal intrusion via automated methods is essential. Recent deep learning-based methods have become popular in solving these problems by generating high detection ability. In this study, the YOLOv5 method is proposed to detect four categories of animals commonly involved in farming intrusion areas. YOLOv5 can generate high accuracy in detection using cross stage partial network (CSP) as a backbone. This network is employed to extract the beneficial characteristics from an input image. The results of the implementation of this method show that it can detect animal intrusion very effectively and improve the accuracy of detection by nearly 94% mAP. The results demonstrate that the proposed models meet and reach state-of-the-art results for these problems.
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