• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1759-1766
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• 2003

This paper proposes a neuro­fuzzy controllers for trajectory tracking control of robot manipulators. The computed torque method is an effective means for trajectory tracking control. However, the tracking performance of this method is severely affected by the uncertainties of robot manipulators. Therefore, the proposed controller is used to compensate the uncertainties of robot manipulators. In the neuro­fuzzy controllers, the number of fuzzy rules used forty­nine. The effectiveness of the proposed controllers is demonstrated by computer simulations using two­link robot manipulator, As a result, it is confirmed that the output of the proposed neuro­fuzzy controllers can efficiently decrease the uncertainties of robot manipulator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.756-765
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• 2001

An effective dexterous motion control method of redundant robot manipulators based on neural optimization network is proposed to satisfy multi-criteria such as singularity avoidance, minimizing energy consumption, and avoiding physical limits of actuator, while performing a given task. The method employs a neural optimization network with parallel processing capability, where only a simple geometric analysis for resolved motion of each joint is required instead of computing of the Jacobian and its pseudo inverse matrix. For dexterous motion, a joint geometric manipulability measure(JGMM) is proposed. JGMM evaluates a contribution of each joint differential motion in enlarging the length of the shortest axis among principal axes of the manipulability ellipsoid volume approximately obtained by a geometric analysis. Redundant robot manipulators is then controlled by neural optimization networks in such a way that 1) linear combination of the resolved motion by each joint differential motion should be equal to the desired velocity, 2) physical limits of joints are not violated, and 3) weighted sum of the square of each differential joint motion is minimized where weightings are adjusted by JGMM. To show the validity of the proposed method, several numerical examples are illustrated.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.130-139
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• 2002

In this paper, at first, we investigate existing algorithms for finding the minimum infinity-norm solution of consistent linear equations and then propose a new algorithm. The proposed algorithm is intended to includes the advantages of computational efficiency as well as geometric explicitness. As a practical application example, optimum trajectory planning for redundant robot manipulators is considered. Also, an efficient approach avoiding discontinuity in trajectory is proposed by resolving the non-uniqueness problem of minimum infinity-norm solution. To be specific, the proposed method for checking possible discontinuity does not need any other algorithms in checking the possibility of discontinuity while previous work needs specially designed checking courses. To show the usefulness of the proposed techniques, an example calculating minimum infinity-norm solution for comparing the computational efficiency as well as the trajectory planning for a redundant robot manipulator are included.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.8
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• pp.15-23
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• 1992

Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, two kinds of neurocontrol architectures for the dynamic control of robot manipulators are developed. One is based on a System Identification and Control scheme and the other is based on the Feedback-Error leaming scheme. Both of the proposed architectures use an inverse dynamic neurocontroller in parallel with a linear neurocontroller. The difference is that the first architecture uses the system identifier to get the signals used for training neurocontrollers, while the second architecture uses a properly defined energy function. Compared with the previous types of neurocontrollers which are using an inverse dynamic neurocontroller and a fixed PD gain controller, the proposed architectures not only eliminate the painful process of the fixed gain tuning but also exhibit superior peformances because the linear neurocontroller can adapt its gains according to the applied task. This superior performance is tested and verified through computer simulation of the dynamic control of the PUMA 560 arm.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.669-672
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• 2012

This paper investigates an algorithm for robust fault diagnosis in robot manipulators. The TOSM (Third Order Sliding Mode observer) provides both theoretically exact observation and unknown fault identification without filtration. The EOI (Equivalent Output Injections) of the TOSM observers can be used as residuals for the problem of fault diagnosis and to identify the unknown faults. The obtained fault information can be used for fault detection, isolation as well as fault accommodation to the self-correcting failure system. The computer simulation results for a PUMA 560 robot are shown to verify the effectiveness of the proposed strategy.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.73-76
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• 1996

An impedance control approach based on an extended task space formulation is addressed to control the kinematically redundant manipulators. Defining a weighted inner product in joint space, a minimal parametrization of the null space can be achieved and we can visualize the null space motion explicitly. Based on this formulation, we propose a control method called inertially decoupled impedance controller to control the motion of the end-effector as well as the internal motion expanding the conventional impedance control. Some numerical simulations are given to demonstrate the performance of the proposed control method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.99-102
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• 1996

A novel iterative learning control scheme comprising a unique feedforward learning controller and a disturbance observer is proposed. Disturbance observer compensates disturbance due to parameter variations, mechanical nonlinearities, unmodeled dynamics and external disturbances. The convergence and robustness of the proposed controller is proved by the method based on Lyapunov stability theorem. The results of numerical simulation are shown to verify the effectiveness of the proposed control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.76-81
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• 2004

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.603-608
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• 2003

Recently, personal robots and home robots are developing by many companies and research groups. It is considered that a general effective interface for user of those robots is speech or voice. In this paper, Japanese speech based man-machine interface system is discussed for reflecting the fuzziness of natural language on robots, by using fuzzy reasoning. The present system consists of the derivation part of action command and the modification part of the derived command. In particular, a unique problem of Japanese is solved by applying the morphological analyzer ChaSen. The proposed system is applied for the motion control of a robot manipulator. It is proved from the experimental results that the proposed system can easily modify the same voice command to the actual different levels of the command, according to the current state of the robot.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.702-712
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• 2008

This paper presents a novel approach for controlling electrically driven robot manipulators based on voltage control. The voltage-based control is preferred comparing to torque-based control. This approach is robust in the presence of manipulator uncertainties since it is free of the manipulator model. The control law is very simple, fast response, efficient, robust, and can be used for high-speed tracking purposes. The feedback linearization is applied on the electrical equations of the dc motors to cancel the current terms which transfer all manipulator dynamics to the electrical circuit of motor. The control system is simulated for position control of the PUMA 560 robot driven by permanent magnet dc motors.