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RISS 인기검색어
- 검색키워드
- 저자 : Bambang Riyanto Trilaksono (검색결과 2 건)
- 무료
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Mardiati Rina,Trilaksono Bambang Riyanto,Wibowo Sony Sulaksono,Laila Dina Shona 한국자동차공학회 2021 International journal of automotive technology Vol.22 No.4
This paper proposes a novel MDP framework to deal with the accuracy of the motorcycle driving model. It proposes a weighted and unweighted Dynamical-Discretized Reward Field (DDRF) as a major contribution on modeling motorcycle maneuver in mixed traffic conditions. Other contributions of this work are the integration of a motorcycle trajectory maneuver model in the state transition function, derivation of probability functions, area of awareness (AoA) and its sectorization to perceive vehicles inside the AoA, which is used to determine actions. We conducted some simulations to evaluate the performance of the proposed model by comparing the data from the simulations with real data. In this study, we use 100 simulation data on motorcycle maneuvering, which consisted of two different scenarios, i.e., 50 data of motorcycle maneuvering to avoid other motorcycles and 50 data of motorcycle maneuvering to avoid cars. We adjusted the simulation setting to the real situation and measured the performance of the proposed model using root mean square error (RMSE). In general, the proposed method can properly model the maneuver of motorcycles in heterogeneous traffic with an RMSE value of around 0.74 meters. This model performs twice as good as an existing car-following model. Furthermore, the proposed reward function performs around 4 ~ 6 % better than the reward function in previous studies.
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The Ball and Beam System: Cascaded LQR-FLC Design and Implementation
Muhammad Ridho Rosa,Muhammad Zakiyullah Romdlony,Bambang Riyanto Trilaksono 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.1
This paper aims to derive a dynamic model of ball and beam systems (BBS), design, and implement the optimal controller linear quadratic regulator (LQR) with cascaded feedback linearization controller (FLC). It is known that missing information of the parameter of the system model can cause undesired tracking results. The LQR-FLC can solve the tracking problem caused by model uncertainty. The LQR controller, the inner-loop controller, is designed based on the derived BBS dynamics model. Then FLC, the outer-loop controller, is designed to get the desired tracking error dynamics. By using LQR-FLC, one can use the simple PD controller to design the tracking error dynamics. In addition, the estimator is designed to provide the controller with the full-states information. This research is worth investigating because the LQR-FLC scheme for controlling the BBS is missing in the literature. The experiment is carried out using a hardware in loop (HIL) scheme where MATLAB-Simulink is connected to a microcontroller. Simulation and experimental results show the effectiveness of the proposed cascaded LQR-FLC.
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