An investigation of energy efficiency of a wheel loader with automated manual transmission

Kwangseok Oh,Seung Jae Yun,Kyungeun Ko,김판영,Jaho Seo,Kyongsu Yi 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.7

This paper describes an investigation of energy efficiency by applying an advanced powertrain system in a conventional wheel loader. A conventional powertrain of a wheel loader consists of an engine, torque converter and transmission. A torque converter in a conventional system generally causes a significant amount of energy loss, as determined by analyzing energy flow based on V-pattern working. To prevent energy loss in a torque converter, Automated manual transmission (AMT) was proposed and modeled in this paper as an advanced powertrain. A wheel loader based on AMT does not need to use a torque converter since the single clutch system is used between the engine and transmission with subsystems of engine controller, clutch actuator and controller. A simplified single clutch system and controller were constructed for V-pattern working of a wheel loader. Additionally, a PI-controller was used as a control algorithm for engine speed control to prevent energy loss while the clutch is not engaged. All simulation models have been constructed in the Matlab/Simulink environment, and simulation studies were conducted by using a simulation model of a wheel loader with a driver model based on V-pattern working. Simulation results of the AMT-based wheel loader were analyzed by comparison with the results of the torque converter-based wheel loader, and the results show that the AMT-based wheel loader is more energy efficient than the conventional wheel loader.

Development of a Human-Like Learning Frame for Data-Driven Adaptive Control Algorithm of Automated Driving

Kwangseok Oh,Sechan Oh,Jongmin Lee,Kyongsu Yi 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10

This paper proposes a human-like learning frame for data-driven adaptive control algorithm of automated driving. Generally, driving control algorithms for automated vehicles need environment information and relatively accurate system information like mathematical model and system parameters. Because there are unexpected uncertainties and changes in environment and system dynamic, derivation of relatively accurate mathematical model or dynamic parameters information is not easy in real world and it can have a negative impact on driving control performance. Therefore, this study proposes data-driven feedback control method for automated driving based on human-like learning frame in order to address the aforementioned limitation. The human-like learning frame is based on finite-memory like human and is divided into two parts such as control and decision parts. In the control part, it is designed that feedback gains are derived based on least squares method using saved error states and gains in finite-memory. And the control input has been computed using the derived feedback gains. After control input is used for driving control, it is designed that current error states and the used feedback gains are saved in the finite-memory real-time in the decision part if the time-derivative of cost function has a negative value. If the time-derivative of the cost function has greater than or equal to zero, it is designed that the feedback gains are updated using gradient descent method with sensitivity estimation and the used error states and gains are saved in the memory as a new data. The performance evaluation has been conducted using the Matlab/Simulink and CarMaker software for reasonable evaluation.

Sensitivity-based model-free adaptive displacement and velocity control algorithms for unknown single-input multi-output systems with recursive least squares

Kwangseok Oh,서자호 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.11

This study presents sensitivity-based adaptive model-free adaptive displacement and velocity control algorithms for unknown single-input multi-output (SIMO) systems using recursive least squares (RLS) with exponential forgetting. Nonlinearities in real-world systems make predictions of the dynamic behaviors of systems challenging and have a negative influence on the performance of control systems. To address this problem, the sensitivity-based model-free adaptive control algorithm, which does not require a mathematical model of a system, is proposed in this study. In the algorithm, a feedback control law for a SIMO system was designed with multiple adaptive feedback gains. The virtual function that represents the relationship between multiple feedback gains and control errors was also designed to construct an adaptation rule with a linear combination. The coefficients in the virtual function were estimated in real-time using RLS with multiple exponential forgetting factors. By using the estimated coefficients, the adaptation rule for feedback gains with the gradient descent method was designed, which can be automatically adjusted through applying Lyapunov’s direct method. The performance of the proposed model-free adaptive control algorithm was evaluated in an actual DC motor system under angular displacement and velocity tracking control scenarios. Evaluation results show that the designed control algorithm enables the system to track the desired output successfully without system modeling.

LQR-based Adaptive Steering Control Algorithm of Multi-Axle Crane for Improving Driver’s Steering Efficiency and Dynamic Stability

Kwangseok Oh,Jaho Seo,Jeong-Woo Han 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10

The conventional steering principle (Ackerman theory) for existing steering systems of a multi-axle cane determines the steering angle of each axle by focusing on minimizing the slip angles rather than enhancing a driver’s steering efficiency. Therefore, this paper proposed a LQR-based adaptive steering control algorithm of a multi-axle crane which solves the optimal steering angles to improve the driver’s steering efficiency. For this, a crane error dynamics model was derived and simulation studies were conducted to evaluate the proposed controller’s performance for the cases of single-lane-change and curved path scenarios. The simulation results present that the proposed steering control strategy improves the steering efficiency by decreasing the driver’s steering effort, and dynamics stability by reducing the yaw rate.

Regorafenib prevents the development of emphysema in a murine elastase model

Kwangseok Oh,Gun-Wu Lee,Han-Byeol Kim,Jin Hee Park,Eun-YoungShin,Eung-GookKim 생화학분자생물학회 2023 BMB Reports Vol.56 No.8

Emphysema is a chronic obstructive lung disease characterized by inflammation and enlargement of the air spaces. Regorafenib, a potential senomorphic drug, exhibited a therapeutic effect in porcine pancreatic elastase (PPE)-induced emphysema in mice. In the current study we examined the preventive role of regorafenib in development of emphysema. Lung function tests and morphometry showed that oral administration of regorafenib (5 mg/kg/day) for seven days after instillation of PPE resulted in attenuation of emphysema. Mechanistically, regorafenib reduced the recruitment of inflammatory cells, particularly macrophages and neutrophils, in bronchoalveolar lavage fluid. In agreement with these findings, measurements using a cytokine array and ELISA showed that expression of inflammatory mediators including interleukin (IL)-1β, IL-6, and CXCL1/KC, and tissue inhibitor of matrix metalloprotease-1 (TIMP-1), was downregulated. The results of immunohistochemical analysis confirmed that expression of IL-6, CXCL1/KC, and TIMP-1 was reduced in the lung parenchyma. Collectively, the results support the preventive role of regorafenib in development of emphysema in mice and provide mechanistic insights into prevention strategies.

An Investigation on Steering Optimization for Minimum Turning Radius of Multi-Axle Crane Based on MPC Algorithm

Kwangseok Oh,Jaho Seo,Jinho Kim,Kyongsu Yi 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10

The study investigates on the optimized steering wheel angle for minimum turning radius of all-terrain crane by applying a model predictive control (MPC) strategy. For this, the simplified linear bicycle model and error dynamic model are firstly derived for the crane with a multi-axle steering system. Then, MPC controller is designed with an optimal objective function to minimize the vehicle dynamic error for minimum turning radius. The minimum turning radius and corresponding optimized steering angle at different vehicle speeds are analyzed in the MATLAB/Simulink environment.

Online Estimation of Rotational Inertia of an Excavator Based on Recursive Least Squares with Multiple Forgetting

Kwangseok Oh,Kyong Su Yi,Jaho Seo,Yongrae Kim,Geunho Lee 유공압건설기계학회 2017 드라이브·컨트롤 Vol.14 No.3

This study presents an online estimation of an excavator’s rotational inertia by using recursive least square with forgetting. It is difficult to measure rotational inertia in real systems. Against this background, online estimation of rotational inertia is essential for improving safety and automation of construction equipment such as excavators because changes in inertial parameter impact dynamic characteristics. Regarding an excavator, rotational inertia for swing motion may change significantly according to working posture and digging conditions. Hence, rotational inertia estimation by predicting swing motion is critical for enhancing working safety and automation. Swing velocity and damping coefficient were used for rotational inertia estimation in this study. Updating rules are proposed for enhancing convergence performance by using the damping coefficient and forgetting factors. The proposed estimation algorithm uses three forgetting factors to estimate time-varying rotational inertia, damping coefficient, and torque with different variation rates. Rotational inertia in a typical working scenario was considered for reasonable performance evaluation. Three simulations were conducted by considering several digging conditions. Presented estimation results reveal the proposed estimation scheme is effective for estimating varying rotational inertia of the excavator.

Online Estimation of Rotational Inertia of an Excavator Based on Recursive Least Squares with Multiple Forgetting

Oh, Kwangseok,Yi, Kyong Su,Seo, Jaho,Kim, Yongrae,Lee, Geunho The Korean Society for Fluid Power and Constructio 2017 드라이브·컨트롤 Vol.14 No.3

This study presents an online estimation of an excavator's rotational inertia by using recursive least square with forgetting. It is difficult to measure rotational inertia in real systems. Against this background, online estimation of rotational inertia is essential for improving safety and automation of construction equipment such as excavators because changes in inertial parameter impact dynamic characteristics. Regarding an excavator, rotational inertia for swing motion may change significantly according to working posture and digging conditions. Hence, rotational inertia estimation by predicting swing motion is critical for enhancing working safety and automation. Swing velocity and damping coefficient were used for rotational inertia estimation in this study. Updating rules are proposed for enhancing convergence performance by using the damping coefficient and forgetting factors. The proposed estimation algorithm uses three forgetting factors to estimate time-varying rotational inertia, damping coefficient, and torque with different variation rates. Rotational inertia in a typical working scenario was considered for reasonable performance evaluation. Three simulations were conducted by considering several digging conditions. Presented estimation results reveal the proposed estimation scheme is effective for estimating varying rotational inertia of the excavator.

Functional perspective-based probabilistic fault detection and diagnostic algorithm for autonomous vehicle using longitudinal kinematic model

Oh, Kwangseok,Park, Sungyoul,Lee, Jongmin,Yi, Kyongsu Springer-Verlag 2018 Microsystem Technologies Vol.24 No.11

Development and evaluation of advanced safety algorithms for excavators using virtual reality

Oh, Kwangseok,Kim, Hyungki,Seo, Jaho,Cha, Moohyun,Lee, Geunho,Yi, Kyong-Su Springer-Verlag 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.3