Hakgu Kim

Development of supervisory control strategy for optimized fuel consumption of the compound hybrid excavator

This paper describes a hybrid supervisory control strategy for fuel optimization of a compound hybrid excavator that incorporates an engine-assist motor, an electric swing motor and a super capacitor in the original drive train. The main features of the compound hybrid excavator are that the electrically-propelled swing motor increases the number of energy paths and also incurs many constraints related to the power balance of the hybrid drive train. The dynamic programming technique has been applied to the constrained nonlinear fuel optimization problem over representative excavation cycles. Then, by imitating the behaviour of the dynamic programming optimization scheme, a rule-based controller has been designed. The rule-based controller determines the diesel engine set speed and the engine-assist motor power to operate the engine in an efficient region and to minimize the energy loss in the hybrid drive train. The performance of the rule-based controller has been compared to that of the thermostat controller, which determines the energy distribution, only based on the state of charge of the applied super capacitor. Simulation results indicate that the compound hybrid excavator can improve overall fuel efficiency by about 20% compared to the conventional excavator for representative excavation cycles, and the proposed rule-based controller further enhances the fuel economy by about 2–3% compared to the simple thermostat controller.

Design of a model reference cruise control algorithm

A methodology to design a model free cruise control algorithm(MFCC) is presented in this paper. General cruise control algorithms require lots of vehicle parameters to control the power train and the brake system, that makes control system complicate. Moreover, when the target vehicle is changed, the vehicle parameters should be reinvestigated in order to apply the cruise control algorithm to the subject vehicle. To overcome these disadvantages of the conventional cruise control algorithm, MFCC algorithm has been developed. The algorithm directly determines the throttle, brake inputs based on the reference model parameters such as clearance, relative velocity, and subject vehicle acceleration. This simple structure facilitates human centered design of cruise controller and makes it easy to apply control algorithm to various vehicles without reinvestigation of vehicle parameters. To achieve vehicle safety and driver comfort, control parameters of the model free cruise control algorithm has been designed using the real-world driving test data. Few parameters have been adapted using a learning algorithm to minimize the effects of unawareness of the vehicle parameters. From the simulation study, the performance of the proposed controller has been compared with that of the conventional adaptive cruise controller(ACC). It is shown that the proposed control strategy its behavior is not only similar to that of the conventional ACC in normal driving condition but also more robust than the ACC for model uncertainty and external disturbance. Language: en

Time-Varying Parameter Adaptive Vehicle Speed Control

This paper presents a novel approach for time-varying parameter adaptive throttle and brake control for vehicle speed tracking. A control algorithm has been developed based on a linearized longitudinal vehicle model with characteristic lumped parameters. The lumped parameters are slowly time varying, except when a vehicle experiences gear shift. Combined parameter adaptation and throttle/brake control algorithm have been developed. The performance of the proposed control algorithm has been evaluated via simulations and vehicle tests. Since the proposed control algorithm has been designed using a generic form of the vehicle model, it can be implemented for different classes of vehicles with no information about the vehicle powertrain and the brake system. It has been shown from both simulations and vehicle tests that the vehicle speed tracking performance is robust to external disturbance.

Parameter Adaptive Steering Control for Autonomous Driving

This paper describes parameter adaptive steering control for autonomous driving. The proposed controller has been developed based on preview model of the upcoming road and on yaw rate gain which is yaw rate response to the front steering angle input. The yaw rate gain is dependent on vehicle velocity and understeering gradient. The proposed controller adapts the yaw rate gain and determine proper steering wheel angle for the tracking of desired yaw rate. Since the proposed controller is based on adapting yaw rate gain, it can be applied for any vehicles without any information on vehicle parameters. It has been shown from both simulations and vehicle tests that good path tracking performance can be obtained by the use of the proposed steering controller without vehicle parameters.

Combined throttle and brake control for vehicle cruise control: A model free approach

This paper has been focused on the design of throttle and brake controller for vehicle speed control. The control goals of the speed control are reduction of the effects of model uncertainty and external disturbance caused by the nonlinearity of the servo-level dynamics and the unpredictable driving resistance such as inclination of road and aero dynamic drag force. The tracking performance also should be guaranteed. To that end, a model free approach has been proposed in this paper. The proposed controller uses modified linear longitudinal vehicle model with reasonable assumptions to derive throttle and brake control law and a few parameters in the vehicle model has been defined to represent the system delay and variation of the vehicle parameters during driving. Since the defined parameters named vehicle characteristic variables(VCVs) vary depending on the vehicle states, an adaptation algorithm has been developed to estimate the VCVs. The error dynamics of the vehicle acceleration and VCVs have been analyzed to prove the stability of the proposed algorithm. The tracking performance of the model free cruise controller has been verified by simulation. The results not only show good tracking performance but also verify that the MFCC considerably reduces the effects of model uncertainty and external disturbance using adaptation algorithm.

Development and Validation of Wheel Loader Simulation Model

건설기계를 이용한 대규모 토목공사에서 휠로더Key Words: Driving Powertrain(주행부 동력전달), Hydraulic Powertrain(유압부 동력전달), Hydraulic Cylinder Model(유압 실린더 모델), Main Relief Valve(메인 릴리프 밸브), Main Control Valve(메인 릴리프 밸브), Check Valve(체크밸브), Parallel Axis Theorem(평행축 정리) 초록: 본 논문은 Matlab/simulink 기반 휠로더 시뮬레이션 모델의 개발과 검증에 대한 논문이다. 휠로더 시뮬레이션 모델의 개발 및 검증은 실제 휠로더의 생산단계에 앞서 휠로더의 성능을 평가하고 개선하기 위한 목적을 두고 있다. 휠로더 시뮬레이션 모델은 전체적으로 주행부/유압부 동력전달계 모델, 주행부/작업장부 동역학 모델을 포함한4가지 모델로 나뉘어져 있다. 휠로더의 주행 및 작업성능을 평가하고 개선하기 위해서는 언급된 4 가지 모델의 통합 시뮬레이션이 필요하며 통합된 시뮬레이션 모델은 성능평가 외의 연료효율의 최적화, 하이브리드 시스템 및 지능형 휠로더 모델의 개발로써 작업효율 향상에 기여할 수 있을 것이다. 본 논문에 제안된 시뮬레이션 모델은 주행부와 작업부 실험 데이터와의 비교를 통해 검증 되었다. Abstract: This paper presents the development and validation of a wheel loader simulation model. The objective of doing so is to evaluate the performance of the wheel loader and improve its overall performance using Matlab/Simulink. The wheel loader simulation model consists of 4 parts: mechanical/hydraulic powertrain model and vehicle/working dynamic model. An integrated simulation model is required to evaluate and improve the performance of the wheel loader. It is expected that this model will be applied to fuel economizing, improving the pace of operation by using the hybrid system, and the intelligent wheel loader. The performance of the proposed simulation model has been validated by using Matlab/Simulink to compare the driving and the working experimental data.

Development of Power Management Strategies for a Compound Hybrid Excavator

: 패널티 함수 s : 변환효율 Key Words : Compound Hybrid Excavator(복합형 하이브리드 굴삭기), Power Management Strategy(동력 제어 전략), ECMS Strategy(실시간 최적 제어 기법) 초록: 본 논문은 복합형 하이브리드 굴삭기를 위한 동력전달계 제어기법에 대하여 기술하였다. 하이브리드 굴삭기는 기존 굴삭기의 동력전달계를 하이브리드화 하여 연비향상 및 배출가스 저감을 목표로 개발되고 있다. 특히 복합형 하이브리드 굴삭기는 유압시스템의 일부를 전기시스템으로 대체하여 낮은 유압효율로 인한 에너지 손실을 줄일 수 있도록 구성되어 있다. 해당 굴삭기의 하이브리드 동력 제어기는 동력전달계의 동력 흐름을 관리하여 굴삭기의 연비를 향상 시키고, 슈퍼 커패시터의 충전량을 적절한 범위에서 유지하며, 기존 굴삭기에 준하는 성능을 유지하여야 한다. 이를 위하여 본 논문에서는 슈퍼 캐패시터의 충전량 기반의 서모스탯(Thermostat)형 제어기와 실시간 최적해를 이용한 ECMS제어기를 설계하였으며 시뮬레이션을 통하여 그 성능을 검증하였다. 시뮬레이션 결과, 하이브리드 굴삭기의 연비가 대략 20% 이상 향상될 것으로 기대되며, 특히 등가 연료 개념을 이용한ECMS제어기의 성능이 서모스탯(Thermostat)형 제어기에 비해 연비 및 슈퍼 커패시터 충전량 관리 측면에서 보다 향상된 것을 확인하였다. Abstract: This paper presents the power management strategies for a compound hybrid excavator. The compound hybrid excavator has been replaced the hydraulic swing motor to the electric swing motor. This excavator requires a proper control algorithm to regulate the energy flow between the mechanical coupling and the electric devices. The controller should improve fuel economy and maintain the super capacitor voltage within a proper range. A thermostat controller and ECMS controller are designed such that these objectives can be achieved. The thermostat controller regulates the power of the engine-assist motor on the basis of the super capacitor voltage, and the ECMS controller determines it using the real-time fuel minimization strategy based on the concept of equivalent fuel. Simulation results showed that by using the hybrid excavator, the fuel economy becomes about 20% higher than that obtained using the conventional excavator and that the ECMS controller outperforms the thermostat controller.

Lateral Control for Automated Vehicle Following System in Urban Environments

In contrast to highway, there are some sections not well maintained in urban roads. In these sections, there may be faint lane marks or static obstacles due to construction or some other reasons. Therefore, an automated vehicle following system such as traffic jam assistant should consider these sections to guarantee the safety of the system. In order to achieve this purpose, a model predictive control (MPC) scheme has been developed. The objectives of MPC are to compute the sequence of optimal steering input for vehicle following with obstacle avoidance. For this, the MPC uses the lead vehicles state and obstacles position obtained by lidars. For this purpose, a simplified nonlinear model of the vehicle was used to predict the future evolution of the system. Based on this prediction, performance index is optimized under operating constraints at each time step. A test vehicle equipped with two lidars on left and right corner of the front bumper has been developed. And the performance of the proposed MPC-based steering control algorithm has been investigated via vehicle test. Test results show the robust performance of vehicle following in urban environments. Language: en

Design of Near-Minimum Time Path Planning Algorithm for Autonomous Driving

This paper presents a near-minimum time path planning algorithm for autonomous driving. The problem of near-minimum time path planning is an optimization problem in which it is necessary to take into account not only the geometry of the circuit but also the dynamics of the vehicle. The path planning algorithm consists of a candidate path generation and a velocity optimization algorithm. The candidate path generation algorithm calculates the compromises between the shortest path and the path that allows the highest speeds to be achieved. The velocity optimization algorithm calculates the lap time of each candidate considering the vehicle driving performance and tire friction limit. By using the calculated path and velocity of each candidate, we calculate the lap times and search for a near-minimum time path. The proposed algorithm was evaluated via computer simulation using CarSim and Matlab/Simulink.

Development of supervisory control algorithm for compound hybrid excavator

This paper presents a supervisory control strategy for a compound type hybrid excavator. The compound type hybrid drive train means that the parts of the hydraulic actuators are replaced by electric motors. Therefore, the required power of the replaced actuator should be provided by the electric energy storage device. This structure makes it difficult to design the supervisory control strategy, since the consumed energy by the electric motor causes the charge sustaining problem. To that end, hybrid supervisory control algorithm has been proposed in this paper. The algorithm composed of two parts: the optimal power distribution controller and engine set speed controller. The optimal power distribution controller determines engine assist motor power by considering the charge sustaining and fuel economy. The engine set speed control strategy determines engine set speed based on the pump load to shift engine operating points to the optimal operating line. The performance of the proposed supervisory controller has been compared with the thermostat controller using simulation. The simulation results indicate that the proposed controller improves 2-3% of fuel efficiency compared the thermostat controller and show excellent charge sustaining performance.