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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.
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.
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.
Kwangseok Oh,Eunhyek Joa,Jisoo Lee,Jaemin Yun,Kyongsu Yi 한국자동차공학회 2019 International journal of automotive technology Vol.20 No.5
This paper describes a yaw stability control algorithm of 4WD vehicles based on model predictive torque vectoring with physical constraints. A vehicle planar model based predictive rear and all-wheel torque vectoring algorithms were developed for 4WD vehicles by considering predictive states and driver’s steering wheel angle. The physical constraints applied to the model predictive control consist of three types: limitation on magnitude of tire force, change rate of tire force, and output torque of transfer case. Two types of torque vectoring algorithms, rear-wheel and all-wheel, were constructed for comparative analysis. The steady state yaw rate was derived and applied as a desired value for yaw stability of the vehicle. The algorithm was constructed in a MATLAB/Simulink environment and the performance evaluation was conducted under various test scenarios, such as step steering and double lane change, using the CarSim software. The evaluation results of the predictive torque vectoring showed sound performance based on the prediction of states and driver’s steering angle.
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.
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.
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.
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.