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Self-Tuning Control with Neural Network for Robot Manipulator |
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Nattapon Jaisumroum(KMITL, Thailand) |
| This paper presents an approach of the self-tuning control with neural network for robot manipulator in an object balancing task. A 3DOF robot arm (Novint Falcon 3D haptic) is used to hold a flat plate balancing a cylindrical object put on. Since a neural network algorithm were presented earlier in [1],[2] in order to learn and control the posture of the robot, we now employ the visual feedback into neural network to enable the robot arm learn to move its end effector. A webcam is used to determine position of a cylinder object rolling on a flat plate that the robot is holing. |
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Simulation Control of an Active Suspension System Using Fuzzy control & H∞ Control Methods |
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Khalil Ali Khalil Ibrahim(Assiut University, Egypt), Nouby Ghazaly(South Valley University, Egypt), Ahmed Saad Ali(Assiut University, Egypt) |
| This paper describes fuzzy and H∞ techniques for the automobile active suspension system. The design objective is to provide smooth vertical motion so as to achieve the road holding and riding comfort over a wide range of road profiles. The objective of the proposed control schemes is demonstrated via simulations. The simulation results of the different controllers are compared by using MATLAB /SIMULINK toolbox. Results demonstrate the effectiveness and usefulness of the proposed control method.
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The Stabilization Condition of Continuous Affine Fuzzy Systems Under Imperfect Premise Matching |
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Hyeon Jun Lim, Jin Bae Park(Yonsei University, Korea), Young Hoon Joo(Kunsan National University, Korea) |
| In this paper, the stabilization condition for the continuous time affine fuzzy system is proposed under the imperfect premise matching. This paper employs the specific transformation matrix related to the input matrix. Furthermore, the concept of the imperfect premise matching is considered to reduce the implementation cost caused by complicated membership functions. Finally, the effectiveness of proposed approach is verified with a numerical example. |
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Medical Decision Making Diagnosis System Integrating k-means and Naïve Bayes algorithms |
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Young Im Cho, Aigerim Altayeva, Zharas Suleimenov(Gachon University, Korea) |
| In this paper, by using data mining we can evaluate many patterns which will be use in future to make intelligent systems
and decisions By data mining refers to various methods of identifying information or the adoption of solutions based on
knowledge and data extraction of these data so that they can be used in various areas such as decision-making, the prediction
value for the prediction and calculation. The results
indicate that the integration of the K-means clustering with naïve Bayes with different initial centroid selecting naive
Bayesian improve accuracy in diagnosis of the patient. |
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Autonomous Offshore Container Crane System Using a Fuzzy-PD Logic Controller |
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Ngo Phong Nguyen(Can Tho University of Technology, Viet Nam), Quang Hieu Ngo(Can Tho University, Viet Nam) |
| A Fuzzy-PD control strategy for an offshore container crane is investigated in this study. The control objective during the loading and unloading process is to keep the payload in the desired region in the presence of ship motions. A new control strategy which is a combination of a Fuzzy controller, PD controller and compensation mechanism, is proposed as well. This control scheme guarantees the stability of the closed-loop system. Simulation and experimental results are provided to verify the effectiveness of the proposed control system for offshore container cranes. |
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Fuzzy Logic Model for Degumming and Bleaching Troubleshooting in Palm Oil Refining |
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Nur Syuhada' Mohamad Ali, Khairiyah Mohd Yusof(Universiti Teknologi Malaysia, Malaysia) |
| Failures at degumming and bleaching process in palm oil refining affect plant performance, production delay, and loss to the company. In current practice, troubleshooting in these process relies on human knowledge and trial and error method which can caused other failures and time consuming. Therefore, fuzzy logic model was developed for troubleshooting degumming and bleaching process. Qualitative and numerical data were collected in this study. Centre of gravity method (COG) method was used for defuzzification. The proposed model was shown successfully troubleshooting task. |
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