• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.157-166
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• 2015

In this paper, we proposed a new robust control scheme to implement stable control of robot manipulators including nonlinear perameters The proposed robust controller is composed of a nonlinear controller and linear compemsation controller. It shows a good robust performance in reaching mode which does not possess invariance property. Thus, the proposed nonlinear controller showed a good robust performance in the whole region, It was illustrated that the proposed control showed a good transient response and trajectory tracking performance for robot manipulator with four joint by experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.89-96
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• 2002

A new chattering free PD-sliding mode hybrid control scheme is proposed for robot manipulators. This hybrid controller is composed of a PD controller and a semi-continuous sliding mode controller. It has a good robust performance in reaching mode which does not possess invariance property of sliding mode, and has chattering free characteristics in sliding mode. Thus, the PD-sliding mode hybrid controller has a good robust performance in the whole region. It is shown that the proposed control has a good transient response and trajectory tracking performance for a 2-link SCARA robot manipulator.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.12
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• pp.1601-1609
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• 1988

In the control of the robotic manipulators, the variable structure control method for the set point Regualation has an advantage of the insensitivity about parameter variations and disturbances. When the robotic manipulatores are controlled by a point-to-point scheme, no path constraint is considered. Thus, the variable structure control method will be effectively applied only if the trajectory of the robot hand is estimated precisely. In this paper, the joint trajectories in the joint space and the hand trajectory in the cartesian space are calculated by the variable structure control method, and an algorithm is suggested to elaborate the deviation error of the robot hand from a straight line path. The result of this study will become a base of the effective path planning about robotic manipulators with the variable structure control concept.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.315-322
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• 2009

This paper explains a control and navigation algorithm of a 6-DOF gait rehabilitation robot, which can allow a patient to navigate in virtual reality (VR) by upper and lower limbs interactions. In gait rehabilitation robots, one of the important concerns is not only to follow the robot motions passively, but also to allow the patient to walk by his/her intention. Thus, this robot allows automatic walking velocity update by estimating interaction torques between the human and the upper limb device, and synchronizing the upper limb device to the lower limb device. In addition, the upper limb device acts as a user-friendly input device for navigating in virtual reality. By pushing the switches located at the right and left handles of the upper limb device, a patient is able to do turning motions during navigation in virtual reality. Through experimental results of a healthy subject, we showed that rehabilitation training can be more effectively combined to virtual environments with upper and lower limb connections. The suggested navigation scheme for gait rehabilitation robot will allow various and effective rehabilitation training modes.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1413-1415
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• 2004

This paper proposes a SVM(Support Vector Machine) training algorithm to control a service robot with voice command. The service robot with a stereo vision system and dual manipulators of four degrees of freedom implements a User-Dependent Voice Control System. The training of SVM algorithm that is one of the statistical learning theories leads to a QP(quadratic programming) problem. In this paper, we present an efficient SVM speech recognition scheme especially based on less learning data comparing with conventional approaches. SVM discriminator decides rejection or acceptance of user's extracted voice features by the MFCC(Mel Frequency Cepstrum Coefficient). Among several SVM kernels, the exponential RBF function gives the best classification and the accurate user recognition. The numerical simulation and the experiment verified the usefulness of the proposed algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.155-162
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• 2003

We propose a new technique far real-tine controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Caussian function as a unit function in the fuzzy neural network. and a 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-foray. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.67-76
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• 2005

In this paper, An analysis on the intelligence system for a biped walking robot(BWR) was made and its results were applied to the BWR. Various sensors were applied to the developed BWR for autonomous and intelligent walk in unknown environments. To measure the distance between the object and BWR, ultrasonic sensor and infrared-rays sensor were used. To identity surrounding environments, vision system was used. Gyro sensor was used to control the posture of BWR. Also, piezoelectricity sensor was used to identity the pressure of foot landing on the surface. Sensors applied to the robot have measurement errors according to noises or walking environments. To improve the function of these sensors, influences of noise or sensing errors were minimized using a sensor fusion scheme. A gait test using the sensor fusion system was performed, and its results are presented.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1697-1700
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• 2003

We propose a new technique for real-time controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and a 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 control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three 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.