• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.4
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• pp.62-67
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• 2002

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized loaming architecture. It is Proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tucking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.312-318
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• 2003

We propose a new technique for the cruise control system design of a mobile robot with three drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized teaming architecture. It is proposed a learning controller consisting of too neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by three independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.207-212
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• 2004

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2351-2353
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• 1998

This paper suggests the wheel controller for PWS(Power Wheeled Steering) mobile robot. The proposed controller consists of two parts. To control each motor, the sliding mode controller implemented. This method has robustness about modeling error and disturbance, so the velocity tracking is well guaranteed in the presence of varying load. The design of a fuzzy cross-coupling controller for a PWS mobile robot is described here. Fuzzy cross-coupling control directly minimizes the tracking error by coordinating the motion of the two drive wheels. The fuzzy cross-coupling controller has excellent disturbance rejection and therefore is advantageous when the robot is not loaded symmetrically. The capability of the proposed controller was verified through the computer simulation.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.2
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• pp.171-176
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• 2016

By developing rider robot using omni wheel drive, a usability test for 6 people was conducted after finding out inconvenient factors and ways of improvement. The results of this research are as follows. First, we researched inconvenient factors captured by object of experiment using a rider robot who is living in the dormitory. It showed that the disabled showing 1.6 of satisfaction degree felt more inconvenient for normal people showing 4 of satisfaction degree. It was found that the height of seats is 10 centimeters higher, which caused inconvenience for moving. Second, each of the disabled and normal people showed 2.33 and 2.62 of satisfaction degree below the average for seats, back of a seat, armrest, footrest, security belts. However, for the revised design both of them showed 3.5 of satisfaction degree over the average. Third, most people felt this robot is quite expensive and said they would purchase it if subsidized by the government. Therefore, based on inconvenient factors and ways of improvement found in this research, further study needs to be conducted so as to improve the quality of life of the disabled.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.6
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• pp.498-505
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• 2010

In-Wheel System is a high-efficiency system to supply a new concept of platform which raises the efficiency of motor drive system and applies it to an environment-friendly automobile by installing a highly efficient electric motor directly to wheels and removing factors of power train. The proliferation of these systems is directly related to the safety of our lives, so check the reliability of the part in the development phase and should be certified. Reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time. This paper presents to the verification methods for durability, one of reliability assessments of the Motor, the study calculated acceleration and deceleration torque and the effective torque from driving conditions of In-Wheel Motor, and based on this, it reduced the test time and suggested the verification methods of In-Wheel Motor reliability through the accelerated life test.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1804-1807
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• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learnign architecture. It is proposed a learning controller consisting of two neural networks-fuzzy based on independent reasoning and a connecton net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.82-87
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• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.250-254
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• 2001

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.199-203
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• 2000

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.