• International Journal of Automotive Technology
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• v.7 no.5
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• pp.619-624
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• 2006

This paper presents design of a vehicle stop-and-go cruise control strategy based on analyzed results of the manual driving data. Human drivers driving characteristics have been investigated using vehicle driving data obtained from 100 participants on low speed urban traffic ways. The control algorithm has been designed to incorporate the driving characteristics of the human drivers and to achieve natural vehicle behavior of the controlled vehicle that would feel comfortable to the human driver under low speed stop-and-go driving conditions. Vehicle following characteristics of the cruise controlled vehicle have been investigated using a validated vehicle simulator and real driving radar sensor data.

• Journal of Mechanical Science and Technology
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• v.19 no.4
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• pp.993-1000
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• 2005

A vehicle cruise control strategy designed based on human drivers driving characteristics has been investigated. Human drivers driving patterns have been investigated using vehicle driving test data obtained from 125 participants. The control algorithm has been designed to incorporate the driving characteristics of the human drivers and to achieve natural vehicle behavior of the controlled vehicle that would feel comfortable to the human driver. Vehicle following charac­teristics of the cruise controlled vehicle have been investigated using real-world vehicle driving test data and a validated simulation package.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.359-370
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• 2007

Lane tracking and obstacle avoidance are considered two of the key technologies on an unmanned ground vehicle system. In this paper, we propose a method of lane tracking and obstacle avoidance, which can be expressed as vehicle control, modeling, and sensor experiments. First, obstacle avoidance consists of two parts: a longitudinal control system for acceleration and deceleration and a lateral control system for steering control. Each system is used for unmanned ground vehicle control, which notes the vehicle's location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control strategy of the vehicle can detect obstacle and perform obstacle avoidance on the road, which involves vehicle velocity. Second, we explain a method of lane tracking by means of a vision system, which consists of two parts: First, vehicle control is included in the road model through lateral and longitudinal control. Second, the image processing method deals with the lane tracking method, the image processing algorithm, and the filtering method. Finally, in this paper, we propose a method for vehicle control, modeling, lane tracking, and obstacle avoidance, which are confirmed through vehicles tests.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1023-1030
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• 2002

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.1
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• pp.63-71
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• 2000

A shift control algorithm of a newly developed two-speed gear shift system is proposed for electric vehicle applications. The algorithm is formulated according to the motor torque map and optimized to obtain the adequate propulsion characteristics for vehicle. Two speed gear system with shift control algorithm has proved greater efficiencies in terms of energy economy with its simplified hardware and software structures. The gear shifting is designed to be carried out by an actuator and the control signal from a vehicle control unit equipped with $\mu$-processor. The results of performances and efficiency of the algorithm by simulation and vehicle test are described.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1213-1221
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• 2007

In most case of previous research about vehicle control system, external error occurred by unexpected environmental situation was hardly considered. However, in this paper, to have more accurate position control of differential derive vehicle, we separate the error as an internal error and external error. To calculate the vehicle position in real time, we introduced the Dead-Reckoning algorithms and the simulation result show that the proposed internal and external error control system has fast and accurate position tracking with remarkable diminishment of orientation error. The results reported here can easily be extended to the control of similar type vehicle.

• Journal of Ocean Engineering and Technology
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• v.14 no.2
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• pp.7-12
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• 2000

This paper presents a non-regressor based adaptive control scheme for the trajectory tracking of underwater vehicle-mounted manipulator systems(UVMS). The adaptive control system includes a class of unmodeled effects is applied to the trajectory control of an UVMS. The only information required to implement this scheme ios the upper bound and lowe bound of the system parameter matrices the upper bound of unmodeled effects the number of joints the position and attitude of the vehicle and trajectory commands. The adaptive control law estimates control gains defined by the combinations of the bounded constants of system parameter matrices and of a filtered error equation. To evaluate the performance of the non-regressor based adaptive controller computer simulation was performed with a two-link planar robot model mounted on an underwater vehicle. The hydrodynamic effects acting on the manipulator are included. It is assumed that the vehicle's motion is slow and can be predicted with a proper compensator.

• International Journal of Automotive Technology
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• v.7 no.3
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• pp.303-307
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• 2006

This paper presents the enhancement of vehicle stability by active geometry control suspension(AGCS) system as the world-first, unique and patented chassis technology, which has more advantages than the conventional active chassis control systems in terms of the basic concept. The control approach of the conventional systems such as active suspensions(slow active, full active) and four wheel steering(4WS) system is directly to control the same direction with acting load to stabilize vehicle behavior resulting from external inputs, but AGCS controls the cause of vehicle behaviors occurring from vehicle and thus makes the system stable because it works as mechanical system after control action. The effect of AGCS is the remarkable enhancement of avoidance performance in abrupt lane change driving by controlling the rear bump toe geometry.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.23.1-23
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• 2001

The unmanned vehicle is composed of three parts the front & side sensor system for keeping the lane and avoiding obstacles, the acceleration & brake control system for longitudinal motion control, and the steering control system for the lateral motion control. Each system helps the unmanned vehicle of which should take notice of its location and recognize obstacles around the place by itself and make a decision how much fast to proceed according to circumstances. During the operation, the control strategy that the vehicle can detect obstacles and avoid collision on the road involves with vehicle velocity very much. Therefore, We have to define a traction system which is powered by DC motor so that, unmanned vehicle can control its velocity accurately. In this study, we find mechanical and ...

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.274-284
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• 2019

The control architectures of Chuan Suo (CS) deep submergence rescue vehicle are introduced. The hardware and software architectures are also discussed. The hardware part adopts a distributed control system composed of surface and underwater nodes. A computer is used as a surface control machine. Underwater equipment is based on a multi-board-embedded industrial computer with PC104 BUS, which contains IO, A/D, D/A, eight-channel serial, and power boards. The hardware and software parts complete data transmission through optical fibers. The software part involves an IPC of embedded Vxworks real-time operating system, upon which the operation of I/O, A/D, and D/A boards and serial ports is based on; this setup improves the real-time manipulation. The information flow is controlled by the software part, and the thrust distribution is introduced. A submergence vehicle heeling control method based on ballast water tank regulation is introduced to meet the special heeling requirements of the submergence rescue vehicle during docking. Finally, the feasibility and reliability of the entire system are verified by a pool test.