• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.2
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• pp.15-20
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• 2012

This study is to propose and develop an integrated dynamics control system to improve and enhance the lateral stability and handling performance. To achieve this target, we integrate an AFS and a 4WS systems with a fuzzy logic controller. The IDCS determines active additional steering angle of front wheel and controls the steering angle of rear wheel. The results show that the IDCS improves the lateral stability and controllability on dry asphalt and snow paved road when double lane change and step steering inputs are applied. Yaw rate of the IDCS vehicle tracks reference yaw rate very well and body slip angle is reduced about by 50%. Response time of the IDCS vehicle is also decreased.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.480-489
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• 1999

Design and experimental results of yaw rate controller is described for electricallydriven four wheel vehicle without steering mechanism. Yaw rate controller has been known to be necessary to cope with nonlinear char-acteristics of the wheel/road conditions with respect to different road condition and steering angle. For an effective yaw rate control, a fuzzy PID gain scheduler is considered with changing control parameters. In order to apply proposed algorithm to the system a downsized four wheel drive electrically driven vehicle without steering mechanism was manufactured. With these techniques the proposed yaw rate controller is shown by experiment results to be obtained suficient performance in the whole steering regions.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.1
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• pp.44-54
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• 2023

In this study, the second-order Nomoto's nonlinear expansion model was implemented as a Tagaki-Sugeno fuzzy model based on the heading angular velocity to design the automatic steering system of a ship considering nonlinear elements. A Tagaki-Sugeno fuzzy PID controller was designed using the applied fuzzy membership functions from the Tagaki-Sugeno fuzzy model. The linear models and fuzzy membership functions of each operating point of a given nonlinear expansion model were simultaneously tuned using a genetic algorithm. It was confirmed that the implemented Tagaki-Sugeno fuzzy model could accurately describe the given nonlinear expansion model through the Zig-Zag experiment. The optimal parameters of the sub-PID controller for each operating point of the Tagaki-Sugeno fuzzy model were searched using a genetic algorithm. The evaluation function for searching the optimal parameters considered the route extension due to course deviation and the resistance component of the ship by steering. By adding a penalty function to the evaluation function, the performance of the automatic steering system of the ship could be evaluated to track the set course without overshooting when changing the course. It was confirmed that the sub-PID controller for each operating point followed the set course to minimize the evaluation function without overshoot when changing the course. The outputs of the tuned sub-PID controllers were combined in a weighted average method using the membership functions of the Tagaki-Sugeno fuzzy model. The proposed Tagaki-Sugeno fuzzy PID controller was applied to the second-order Nomoto's nonlinear expansion model. As a result of examining the transient response characteristics for the set course change, it was confirmed that the set course tracking was satisfactorily performed.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.338-343
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• 1999

In this paper, the fuzzy neural network for the obstacle avoidance, which consists of the straight-line navigation and the barrier elusion navigation, is proposed and examined. For the straight-line navigation, the fuzzy neural network gets two inputs, angle and distance between the line and the mobile robot, and produces one output, steering velocity of the mobile robot. For the barrier elusion navigation, four ultrasonic sensors measure the distance between the barrier and the mobile robot and provide the distance information to the network. Then the network outputs the steering velocity to navigate along the obstacle boundary. Training of the proposed fuzzy neural network is executed in a given environment in real-time. The weights adjusting uses the back-propagation of the gradient of error to be minimized. Computer simulations are carried out to examine the efficiency of the real time learning and the guiding ability of the proposed fuzzy neural network. It has been shown that the mobile robot that employs the proposed fuzzy neural network navigates more safely with and less trembling locus compared with the previous reported efforts.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.6
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• pp.17-23
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• 2012

An integrated dynamic control (IDCF) with an active front steering system and an active rear braking system is proposed and developed in this study. A fuzzy logic controller is applied to calculate the desired additional steering angle and desired slip of the rear inner wheel. To validate IDCF system, an eight degree of freedom, nonlinear vehicle model and a sliding mode wheel slip controller are also designed. Various road conditions are used to test the performance. The results show that the yaw rate of IDCF vehicle followed the reference yaw rate and reduced the body slip angle, compared with uncontrolled vehicle. Thus, the IDCF vehicle had enhanced lateral stability and controllability.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.182-189
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• 1999

In this paper, we present an ultrasonic sensor based path planning method using fuzzy logic for obstacle avoidance of an intelligent vehicle in unknown environments. Generally, Robot navigation in unknown terrains is a very complex task difficult to control because of the great amount of imprecise and ambiguous sensor information that has to be considered. In this case, fuzzy logic can satisfactorily deal with such information in quite efficient manner. In this study, we propose two fuzzy logic controller which is composed of steering controller and velocity controller respectively. Our object is to develop a fuzzy controller that can enable a mobile robot to navigate from a start point to a goal point without collisions, in the least possible travel time. The ability and effectiveness for the proposed algorithm will be demonstrated by simulation and expeiment.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.6
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• pp.731-737
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• 1999

This paper describes the path tracking control for a mobile robot which has two casters at the front and rear to keep balance and two driving wheels on the left and right sides of its body. Power wheeled steering method is adapted to control heading of the robot. It is very difficult to find appropriate feedback gains when linear regulator control scheme is adapted to path tracking con-trol of this type of robot. Therefore in this paper we propose the path tracking control algorithm using the fuzzy logic control scheme for this type of root. Simulation to prove the validity of the proposed two algorithms is performed. The results are reported as last part in this paper.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.539-546
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• 2007

The important field of research in ship operation is related to the high efficiency of transportation, the convenience of maneuvering ships and the safety of navigation. For these purposes, many intelligent technologies for ship automation have been required and studied. In this paper, we propose an intelligent voice instruction-based learning (VIBL) method and discuss the building of a ship's steering control system based on this method. The VIBL system concretely consists of two functions: a text conversion function where an instructor's inputted voice is recognized and converted to text, and a linguistic instruction based learning function where the text instruction is understood through a searching process of given meaning elements. As a study method, the fuzzy theory is adopted to build maneuvering models of steersmen and then the existing LIBL is improved and combined with the voice recognition technology to propose the VIBL. The ship steering control system combined with VIBL is tested in a ship maneuvering simulator and its validity is shown.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.233-237
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• 2002

Vehicle Dynamics Control(VDC) has been a breakthrough and become a new terminology for the safety of a driver and improvement of vehicle handling. This paper examines the usefulness of a brake steer system (BSS), which uses differential brake forces for steering intervention in the context of VDC. In order to help the car to turn, a yaw moment can be achieved by altering the left/light and front/rear brake distribution. The steering function achieved through BSS can then be used to control lateral position in an unintended road departure system. A 8-DOF non-linear vehicle model including STI tire model will be validated using the equations of motion of the vehicle, and the non-linear vehicle dynamics. Since Fuzzy logic can consider the nonlinear effect of vehicle modeling, Fuzzy controller is designed to explore BSS feasibility, by modifying the brake distribution through the control of the yaw rate of the vehicle. The control strategies developed will be tested by simulation of a variety of situation; the possibility of VDC using BSS is verified in this paper.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.931-942
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• 1999

This paper develops a 3 DOF vehicle model which includes lateral, roll and yaw motion to study a 4WS vehicle. The model is used for the simulation of a 4WS vehicle behavior, and to derive a control algorithm for rear wheel steering. This paper uses a feedforward plus feedback control scheme to compute a rear wheel steering angle. The feedforward control scheme for computing the first rear wheel steering angle uses a gain which is acquired by multiplying a proper value on a gain to maintain a zero sideslip angle. The feedback control scheme for computing the second rear wheel steering angle uses fuzzy logic and model following control scheme. A linear 2 DOF model is used as a reference model for model following control, and is derived from the developed 3 DOF model by neglecting sprung mass roll motion. A reference state variable is yaw rate, and is computed using the linear 2 DOF model. J-turn and lane change maneuver simulation are performed to show the effectiveness of the developed control scheme. The simulation results show that the 4WS vehicle with the developed control scheme has much better performance in yaw rate, lateral acceleration, roll angle, and sideslip angle than the 2WS vehicle. Also, the results show that the performance of the developed control is close to the one of an optimal control which assumes all states are perfect.