Search

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.

In this paper, a self-tuning rule-based position control algorithm is proposed for DC motors with system parameter estimation using the recursive least squares method. First, a mathematical model of the angular position control of a DC motor was derived. Next, the time-varying parameters including the rotational inertia in the model were estimated using the RLS method along with multiple forgetting factors without prior knowledge of the system. Based on the derived model and the parameter estimation, a sliding mode control algorithm was designed by applying a self-tuning rule that enables the magnitude of the voltage input to be adaptively adjusted for improvement of the energy efficiency. The performance of the designed control algorithm was then experimentally evaluated under several different load conditions. Finally, the evaluation results show that the designed controller achieves a satisfactory capability for a DC motor to deal with both tracking accuracy and energy efficiency without prior knowledge of the system.

This paper describes model-based separated fault detection and fault tolerant control of longitudinal autonomous driving using dual-sliding mode observer for functional safety. Internal and environment sensors such as camera or radar are required to measure the acceleration information of the subject vehicle and the relative distance and velocity information between the preceding and subject vehicles in longitudinal autonomous driving. In order to detect the independent fault of each sensor, a dual-sliding mode observer (SMO) is used for fault reconstruction under the assumption that V2V (Vehicle to Vehicle) communication for vehicle driving state is available. The each SMO reconstructs the expected fault in sensor based on discontinuous injection term used for converging output error to zero. Based on the reconstructed fault by each SMO, faults are detected using threshold approach. When the fault is detected, the reconstructed fault is used for fault tolerant control by subtracting to faulty data. The proposed fault detection (FD) and fault tolerant control (FTC) algorithms were evaluated using actual driving data and a three-dimensional (3D) vehicle model with a linear quadratic regulator for following control. The evaluation results are presented and analyzed with regard to fault reconstruction, detection, and tolerant control in four cases wherein two types of faults were applied.

This paper presents a novel design and control method of an active suspension system using a linear pump control–based hydraulic system for a cost-effective application. Various active suspension systems have been proposed and applied to vehicles due to its ability to improve ride comfort and handling performance even though these active suspension systems are not popular because of their complexity, high cost, heavyweight, and low power efficiency. A new type of active suspension actuator system was designed and validated herein based on the methods of actuator sizing and modified control scheme to address the aforementioned issues. System power capability has been analyzed under various dynamics and road conditions. Active suspension actuator components have been designed based on the results. The electro-hydraulic system is powered by a battery to reduce the energy consumption of the system; hence, it is operated by torque on demand. A double-acting linear hydraulic motor pump with a dual rack and pinion has been proposed for hydraulic force control with a simple on/off switch operation. The actuator force has been controlled by continuous linear motor pump displacement control via torque control using a three-phase synchronous brushless alternative current motor. Dynamic performance evaluation of the actuator system has been conducted using AMESIM and actual rig test. Active height and roll control algorithms for the enhancement of vehicle dynamics considering actuator dynamics have also been developed and validated through the rig and real vehicle tests. The evaluation results showed that the linear motor pump–based active suspension system performs as well as the previous complicated hydraulic active suspension system. The new active system proposed in this study was able to improve the vehicle dynamics using cost-effective actuation system significantly.

This study focuses on the development and evaluation of advanced safety algorithms for excavators using virtual reality (VR). An excavator typically operates under a stationary state with its working parts rotating 360° in coordination with nearby workers. During excavation, a fatal accident can occur due to operator carelessness and work site blind spots. Accidents due to collisions with nearby workers have been increasing. Accordingly, we presented safety system algorithms to prevent collisions with surrounding objects and secure the maximum working area in this study. We also evaluated the performance of safety system algorithms using VR. For risk assessment, we calculated the predicted working area through a kinematic analysis of the excavator’s working parts and accordingly conducted target selection of risk factors. We used time-to-collision and warning indices as safety indices for the safety assessment of the selected target and divided the excavator’s working modes into three categories: Safe, warning, and emergency braking modes. Control inputs, such as alarms and braking, were appropriately defined for each working mode. Under warning mode, workers can avoid collisions because a safety system will alert them of dangerous situations through an alarm. Under emergency braking mode, an emergency braking input signal is dispatched with an alarm to automatically prevent collisions. The advanced safety algorithm proposed in the study was developed in MATLAB/Simulink environment. The VR application was developed using a physics engine. On the basis of this application, the performance evaluation of the safety system algorithms was conducted on the frequently occurring sticking scenario.

본 연구는 슬라이딩 모드 관측 및 제어 기반 자율주행 자동차의 종방향 고장 허용 제어 알고리즘에 관한 것이다. 종방향 가속도 센서의 고장 탐지를 위해 슬라이딩 모드 관측기를 이용하여 상대가속도를 재건한다. 재건된 상대가속도 기반 가속도 확률분포를 이용하여 종방향 가속도의 확률적 고장탐지를 수행한다. 고장 탐지 시 재건된 가속도를 이용하여 가상 가속도를 도출하고, 가상 가속도를 허용 제어를 위해 하위 제어기의 가속도 입력으로 사용한다. 상위 제어기의 슬라이딩 모드 제어기는 선행차량 가속도, 가상 가속도, 하위 제어기 제어성능 불확실성에 대해 강건한 요구 가속도를 도출한다. 성능평가를 위해 속도 구간 별 실 주행 데이터와 3차원 차량 동역학 모델을 이용하여 사용하였다. 본 연구에서 제안하는 고장 허용 제어 알고리즘은 향후 자율주행 종방향 고장안전을 위해 적용될 것으로 예상한다.

This paper presents a laser scanner–based predictive safety system for excavators. Blind spots on excavators and operators’ carelessness cause majority of fatal accidents, such as those in which de...

This research deals with assist wheel control to improve the steering efficiency and dynamic stability of the multi-axle crane based on model predictive control. Since multi-axle crane has relatively high inertia and long distance between the axles, it has slow dynamic response, and thus, different steering strategies according to driving speed intervals are required. Specifically, the steering strategy is that the number of wheels fixed mechanically increases to secure dynamic stability as the driving speed increases. However, although this strategy enables to secure stability by slowing down the dynamic response, it also has a weakness to decrease the steering efficiency. If the steering efficiency is decreased, it may result in augmenting an accident rate due to an increase in driver’s fatigue. Therefore, this study suggests a new steering strategy to improve the steering efficiency by simultaneously guaranteeing dynamic stability. The suggested steering control algorithm can enhance both steering efficiency and stability by deriving an assist wheel control input based on model predictive control using a variable weighting factor derived from the driver’s steering input (steering angle on the first axle). Development and performance evaluation of the suggested algorithm were conducted in the MATLAB/Simulink environment, and evaluation results confirmed that the developed algorithm could both enhance the driver’s steering efficiency and secure dynamic stability.

This paper describes stability assist wheel control based on RLS estimation with forgetting in order to improve the dynamic stability of a multi-axle all-terrain crane. Existing multi-axle all-terrain cranes have greater mass and a longer distance between axles than ordinary vehicles, so their rotational inertia is very large. Large rotational inertia implies a slower dynamic response for yaw motion, and it is difficult to achieve the desired yaw motion within an expected amount of time. Therefore, ensuring yaw and lateral dynamic stability is an important theme in research regarding multi-axle all-terrain cranes. In this study, to ensure the crane’s dynamic stability, a simplified linear crane model of a multi-axle all-terrain crane was developed, in which an assist wheel was chosen according to speed, for control. To improve the driving stability, the chosen assistant wheel’s optimal steering angle was calculated through LQR, and to calculate the optimal feedback gain and steering angle, the rotational inertia and lateral velocity were estimated using recursive least square algorithms with forgetting. MATLAB/Simulink based simulations were used to evaluate the performance of the assist wheel controller for improving the crane’s dynamic stability, and the simulation results showed that the proposed stability assist wheel control method improved yaw and lateral dynamic stability over existing steering systems.

All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver’s steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

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.

This paper presents a wheel loader simulation model, validation results and energy flow analysis. The developed simulation model will facilitate the performance evaluation and optimization process in the development stage of a prototype wheel loader. The wheel loader simulation model consists of mechanical and hydraulic powertrain model, multi-body dynamic model and working part dynamic model. The multi-body dynamic model is simplified since the effect of pitch and roll motion of the wheel loader on the energy flow of the powertrain and hydraulic actuator systems is insignificant, a simplified planar model for the dynamic vehicle is good enough for the objective of this study. Every component is modeled and integrated in a simulation package developed using Matlab/Simulink. The simulation model has been validated using experiment data and the validation results show that it effectively represents the actual dynamic characteristic of the target wheel loader. The verified simulation model will be used as a virtual platform to evaluate the performance of alternative powertrain models such as a dual-clutch transmission and hydrostatic transmission. An advanced control system can also be implemented and evaluated using the virtual platform.

This paper presents simulation-based analysis of energy flow of a wheel loader. The objective of this study is to analyze the energy flow of a wheel loader during driving and working. Because the wheel loader powertrain consists of a mechanical and hydraulic powertrain, the generated power from the engine is divided into 2 powertrains. Further, a virtual prediction of energy flow in the powertrains is a key factor in terms of optimal design. Accordingly, the simulation model that is able to predict the virtual energy flow is developed and analyzed in this study. The proposed wheel loader simulation model has been constructed in the Matlab/Simulink environment. It is expected that the developed simulation model will analyze the energy flow and efficiency in the design stage.

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.