• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.226-231
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• 2016

Researching and developing mobile robot are quite important. Autonomous driving of mobile robot is important in various working environment. For its autonomous driving, mobile robot detects obstacles and avoids them. Purpose of this thesis is to analyze kinematics model of the mobile robot and show the efficiency of type-2 fuzzy self-tuning PID controller used for controling steering angle. Type-2 fuzzy is more flexible in verbal expression than type-1 fuzzy because it has multiple values unlike previous one. To compare these two controllers, this paper conduct a simulation by using MATLAB Simulink. The result shows the capability of type-2 fuzzy self-tuning PID is effective.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.473-478
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• 1992

This paper aims to investigate the navigation control of a mobile robot in a confined environment. Steering angle becomes control variable which is computed from the fuzzy control rules. The identification method proposed in this paper presents the fuzzy control rules obtained through modelling of. the driving actions of human operator. The feasibility of the proposed method is evaluated through the application of the identified fuzzy controls rules to the navigation control of a mobile robot which follows the center of a corridor.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.1
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• pp.65-70
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• 2014

An Integrated Dynamics Control system with four wheel Steering (IDCS) is proposed and analysed in this study. It integrates and controls steer angle of front and rear wheel simultaneously to enhance lateral stability and steerability. An active front steer (AFS) system and an active rear steer (ARS) system are also developed to compare their performances. The systems are evaluated during brake maneuver and several road conditions are used to test the performances. The results showed that IDCS vehicle follows the reference yaw rate and reduces side slip angle very well. AFS and ARS vehicles track the reference yaw rate but they can not reduce side slip angle. On split-${\mu}$ road, IDCS controller forces the vehicle to go straight ahead but AFS and ARS vehicles show lateral deviation from centerline.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.155-163
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• 2000

This paper presents a path planning method of the sensor based intelligent vehicle using fuzzy logic controller for avoidance of moving obstacles in unknown environments. Generally it is too difficult and complicated to control intelligent vehicle properly by recognizing unknown terrain with sensors because the great amount of imprecise and ambiguous information has to be considered. In this respect a fuzzy logic can manage such the enormous information in a quite efficient manner. Furthermore it is necessary to use the relative velocity to consider the mobility of obstacles, In order to avoid moving obstacles we must deliberate not only vehicle's relative speed toward obstacles but also self-determined acceleration and steering for the satisfaction of avoidance efficiency. In this study all the primary factors mentioned before are used as the input elements of fuzzy controllers and output signals to control velocity and steering angle of the vehicle. The main purpose of this study is to develop fuzzy controllers for avoiding collision with moving obstacles when they approach the vehicle travelling with straight line and for returning to original trajectory. The ability are and effectiveness of the proposed algorithm are demonstrated by simulations and experiments.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.10
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• pp.58-66
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• 2008

In the development of intelligent vehicles, path tracking of unmanned vehicle is a basis of autonomous driving and automatic navigation. It is very important to find the exact position of a vehicle for the path tracking, and it is possible to get the position information from GPS. However the information of GPS is not the current position but the past position because a vehicle is moving and GPS has a time delay. In this paper, therefore, the moving distance of a vehicle is estimated using a direction sensor and a velocity sensor to compensate the position error of GPS. In the steering control, optimal fuzzy rules for the path tracking can be found through the simulation of Simulink. Real driving experiments show the fuzzy rules are good for the steering control and the position error of GPS is well compensated by the proposed estimation method.

• Journal of Korean Port Research
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• v.15 no.1
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• pp.37-46
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• 2001

Fuzzy logic control(FLC) is composed of three parts : fuzzy rule-bases, membership functions, and scaling factors. Well-defined fuzzy rule-base should contain proper physical intuition on the plant, so are needed lots of experiences of the skillful expert. When membership functions are considered, some parameters on the memberships function such as function shape, support, allocation density should be selected well. The rule of scaling factors is 'scaling'(amplifying or reducing) for both input and output signals of the FLC to fit in the membership function support and to operate the plant intentionally. To get a better performance of the FLC, it is necessary to adjust the parameters of the FLC. In general, the adaptation of the scaling factors is the most effective adjustment scheme, compared with that of the fuzzy rule-base or membership function parameters. This study proposes the adaptation scheme of the scaling factors. When the adaptation is performed on-line, the stability of the adaptive FLC should be guaranteed. The stable FLC system can be designed with stability analysis in the sense of Lyapunov stability. To adapt the scaling factors for the error signals, the concept of the conventional MRAC would be introduced into slightly modified form. A tracking accuracy of the control system would be enhanced by the modified shape and support of the membership function. The simulation is achieved on the pilot plant with the hydraulic steering control of a UCT(Unmanned Container Transporter) of which modeling dynamics have lots of severe uncertainties and modeling errors.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.84-92
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• 2000

Recently, the progress of industrialization has been taken concern of material handling automation. So for, the conveyor belt has been popular for material handling. However, this system has many disadvantages such as the space, cost, etc. In this paper, a new navigation algorithm using fuzzy is introduced. The mobile robot follows a line installed on the roads. These informations are inputted with three approximate sensors. These obtained informations are analyzed with fuzzy control technique fur autonomous steering. Therefore, unlike existing systems, high reliability is guaranteed under bad environment conditions. The installation and maintenance of a line is easily made at lower cost. This developed mobile robot can be applied to material handling automation in manufacturing system, hospital, inter-office document del ivory.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.4
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• pp.12-24
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• 1997

An intelligent steering control system is designed for the steering control of a 4 wheel drive (4WD) electric vehicles without steering mechanism, where the vehicle is assumed to have 3 degree of freedom and input-output feedback linearization is employed. Especially, a fuzzy-rule-based side force estimator is suggested to avoid uncertain highlynonlinearexpression sof relations between side forces and their factors. Also, aneural-network-based predictive compensator is additionally utilized for the vehicle model to be correctly controlled with unstructured uncertainties. The proposed overall control system is numerically shown to be robust against drastic change of the external environments.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2365-2368
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• 2001

This paper describes a fuzzy steering controller for an autonomous mobile robot with MR sensor. Using the magnetic field($B_{x}$, $B_{y}$, $B_{z}$) obtained from the MR sensor, we designed fuzzy controller for driving on the road center. Fuzzy rule base was built to magnetic field($B_{x}$, $B_{y}$, $B_{z}$). 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 simulation. Also, we know that proposed control algorithm was applied to real autonomous mobile robot.

• Journal of Mechanical Science and Technology
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• v.16 no.8
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• pp.1039-1052
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• 2002

This paper presents an application where a Fuzzy-Logic Controller (FLC) is used at a supervisory level to implement mutual coordination of the steering of the two front wheels of a motorcar. The two front wheels are steered by two independent discrete time state feedback controllers with a view to optimize the steering slip angles. The functions of the two controllers are tied together by way of a FLC. Because of the presence of unmodelled dynamics and disturbances acting on the two sides, it is difficult to achieve the desired performance using conventional control systems. This is the primary reason that FLC is emploged to solve the problem. The results show that the implemented system achieved desired coupling between the two independent systems and thereby reduces the difference between the two steered angles.