• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.353-356
• /
• 1997

Vehicle steering system determines the direction of a vehicle. A manual steering system consists of mechanical connections between the steering wheel and tires. Recent power steering system adds an actuator to help a driver to steer easily at low speed. However, at front collision, the driver can be injured by steering shaft and the power steering pump decreases the engine power. To solve these problems, electronic power steering system which connects the steering wheel and tires with electronic connection is proposed, that has advantages such as decrease of engine load and increase of driver safety reactive. Since the ratio between driver's steering torque and steering torque of tires can be controlled freely, the torque which is delivered from the road to the driver through tires and steering wheel can be reshaped to make the driver feel comfortable. In this paper, the ratio of delivering steering torque and the magnitude of force to be delivered from road to driver has been controlled using fuzzy controller, and it's effectiveness has been shown through simulation results.

• Journal of Navigation and Port Research
• /
• v.26 no.2
• /
• pp.193-197
• /
• 2002

This paper suggests a method to design TSK(Takagi-Sugeno-Kang) fuzzy nonlinear control system for automatic steering system which contains the nonlinear component of ship's maneuvering equation. A TSk fuzzy model can be identified using input-output data and represent a nonlinear system very well. A TSK fuzzy controller can be designed systematically from a TSK fuzzy model because the consequent part of TSK fuzzy rule is a linear input-output equation having a constant term. Therefore, this paper suggests the method identifying the TSK fuzzy model and designing the TSK fuzzy controller based on the TSK fuzzy model for ship steering.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.1
• /
• pp.352-362
• /
• 2014

In this paper, a fuzzy logic controller is designed for a DC motor which can be used for navigation control of mobile robots. These mobile robots can be used for agricultural, defense and assorted social applications. The robots used in these fields can reduce manpower, save human life and can be operated using remote control from a distant place. The developed fuzzy logic controller is used to control navigation speed and steering angle according to the desired reference position. Differential drive is used to control the steering angle and the speed of the robot. Two DC motors are connected with the rear wheels of the robot. They are controlled by a fuzzy logic controller to offer accurate steering angle and the driving speed of the robot. Its location is monitored using GPS (Global Positioning System) on a real time basis. IR sensors in the robot detect obstacles around the robot. The designed fuzzy logic controller has been implemented in a robot, which depicts that the robot could avoid obstacle as well as perform its operation efficiently with remote online control.

• Journal of the Korean Institute of Navigation
• /
• v.14 no.4
• /
• pp.17-30
• /
• 1990

Since L.A. Zadeh introduced the theory of fuzzy sets in 1965, E.H. Mamdani applied the theory to the steam engine control in 1974. Since then, scientists have shown a great deal of interests in its application to practical problems and the possibility of the application of the theory a more complicate system has been increasing greatly. In the fuzzy control, the qualitative knowledge and intuition that the operators of a system has acquired through their experience can be logically described by the Linguistic Control Rule(LCR). The algorithm of th control is made of the LCR, and th control of an object is performed by processing this algorithm implementing a computer. in this thesis, the fuzzy controller of the ship's steering system is devided into two systems, namely FC1 and FC2, according to their control function. FC1 is for the course keeping steering, wheress FC2 is for the altering of s ship's course. The characteristics of the control system were investigated through the digital computer simulation and the results were compared with those of the conventional steering system. It was found that the fuzzy control was more efficient than the conventional auto pilot system.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.9 s.180
• /
• pp.2228-2234
• /
• 2000

Steering of the vehicles on a slippery highway is a difficult task for most passenger car drivers. The steering vehicles on slippery roads tend to slide outward with less lateral forces than on nor mal roads. When the drivers notice that their vehicles on a slippery highway start to depart from the cornering lane, most of them make a sudden steering and/or braking, which in turn may induce spin-out and instability on their vehicles. In this paper, an active steering control method is proposed such that the vehicles in slippery roads are steered as if they are driven on the normal roads. In the proposed method, the estimated lateral forces acting on the steering tires are compared with the reference values and the difference is compensated by the active steering method. A fuzzy logic controller is designed for this purpose and evaluated on a steering Hardware-In-the-Loop Simulation (HILS) system. Steering performance results on the slippery curved and sinus roads demonstrate the effectiveness of the proposed controller. This method can be realized with the steer-by-wire concept and is promising as an active safety technology.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.105.5-105
• /
• 2001

This paper describes a fuzzy steering controller for an autonomous mobile robot with MR sensor. Using the magnetic field(Bx, By, Bz) obtained from the MR sensor, we designed fuzzy controller for driving on the road center. Fuzzy rule base was built to magnetic field(Bx, By, Bz). To develop an autonomous mobile robot simulation program, we have done modeling MR sensor, dynamic model of mobile robot and coordinate transformation. A computer simulation of the robot including mobile robot dynamics and steering was used to verify the steering performance of the mobile robot controller using the fuzzy logic Good results were obtained by computer simulation. So, we confirmed the robustness of the proposed fuzzy controller by computer ...

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.5
• /
• pp.417-423
• /
• 2002

In this paper, we propose intelligent steering control system that apply LIBL(Linguistic Instruction Based Learning) method to steering system of ship and take the place of process that linguistic instruction such as officer s steering instruction is achieved via ableman. We embody ableman s suitable steering manufacturing model using fuzzy inference rule by specific method of study, and apply LIBL method to present suitable meaning element and evaluation rule to steering system of ship, embody intelligent steering gear control system that respond more efficiently on officer s linguistic instruction. We presented evaluation rule to constructed steering manufacturing model based on ableman s experience, and propose rudder angle for steering system, compass bearing arrival time, meaning element of stationary state, and correct ableman manufacturing model rule using fuzzy inference. Also, we apply LIBL method to ship control simulator and confirmed the effectiveness.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.93-97
• /
• 2002

In this paper, we propose intelligent steering control system that apply LIBL(Linguistic Instruction Based Learning) method to steering system of ship and take the place of process that linguistic instruction such as officer's steering instruction is achieved via ableman. We embody ableman's suitable steering manufacturing model using fuzzy inference rule by specific method of study, and apply LIBL method to present suitable meaning element and evaluation rule to steering system of ship, embody intelligent steering gear control system that respond more efficiently on officer's linguistic instruction. We presented evaluation rule to constructed steering manufacturing model based on ableman's experience, and propose rudder angle for steering system, compass bearing arrival time, meaning element of stationary state, and correct ableman manufacturing model rule using fuzzy inference. Also, we apply LIBL method to ship control simulator and confirmed the effectiveness.

• Journal of Korean Port Research
• /
• v.6 no.2
• /
• pp.3-24
• /
• 1992

In the fuzzy control of shop the qualitative knowledge and information that the ship's operators have acquired through their experience can be logically described by the Linguistic control Rule (LCR). The algorithm of the control is made of the LCR and the control of the shop is performed by processing this algorithm implementing a computer. The problem in the fuzzy control is that it is very difficult to describe qualitative human knowledge in the LCR correctly. To tackle this difficulty a Fuzzy Neural Network (FNN) was introduced in this paper. The characteristics of the multi-layer FNN control system applied to the ship's steering system is investigated through the computer simulation, and the results were compared with those of the ordinary fuzzy control system of a ship. The results showed that the FNN method is a very effective to translate human knowledge into the LCR.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.17 no.6
• /
• pp.31-37
• /
• 2018

In order to improve stability and controllability of SUV vehicle, Integrated Dynamics Control system with Steering system (IDCS) was developed. Eight degree of freedom vehicle model and front and rear steering system model were used to design IDCS system. It also employs Fuzzy logic control method to design integrate control system. The performance of IDCS was evaluated with two road conditions and several driving conditions. The result shows that SUV vehicle with IDCS tracked the reference yaw rate under all tested conditions. IDCS reduced the body slip angle also. It represents IDCS improves vehicle stability and steerability.