• Journal of Mechanical Science and Technology
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• v.14 no.11
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• pp.1225-1231
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• 2000

This paper proposes a V-shaped Lyapunov function approach for the model-based control of flexible-joint robots, in which a new model-based nonlinear control scheme is designed based on a V-shaped Lyapunov function. The proposed control guarantees global asymptotic stability for link trajectory control while keeping all internal signals bounded. Since joint flexibility is used as a control parameter, the proposed control is not restricted by the degree of joint flexibility and be applied to flexibility-joint, partly-flexibility, or rigid-joint robots without modification. the effectiveness of the proposed control has been by computer simulation.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.292-297
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• 2013

This paper presents a new method of kinematic modeling for autonomous bicycle by using the differential motion transformation. Kinematic model is indispensable to trajectory planning and control for an autonomous mobile robot. The conventional methods of kinematic modeling for an autonomous bicycle depend on intuition by geometry. On the contrary, the proposed method in this paper is based on the systematic differential motion transformation, thus applicable to various types of autonomous bicycles. The differential motion transformation gives Jacobian between two coordinate frames and the velocity kinematics as a result.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.647-649
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• 2000

Brushless DC motors are widely used in many industrial fields as an actuator of robot and driving power motors of electrical vehicle. In this paper adaptive fuzzy sliding mode scheme is developed for velocity control of brushless DC motor. The proposed scheme does not require an accurate dynamic model. yet it guarantees asymptotic trajectory tracking despite torque variations. Numerical simulation and DSP-based experimental works for velocity control of brushless DC motor are carried out.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.152-155
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• 1994

The presence of joint elasticity or the arm flexibility causes low damped oscillatory position error along a desired trajectory. We utilize a stochastic model for describing the fast dynamics and the approximation error. A second order shaping filter is synthesized such that its spectrum matches that of the fast dynamics. Augmenting the state vector of slow part with that of shaping filter, we obtain a nonlinear dynamics to which a Gaussian white noise is injected. This modeling approach leads us to the design of an extended Kalman filter(KEF) and a linear quadratic Gaussian(LQG) control scheme. We present the simulation results of this control method. The simulation results show us that our Kalman filtering approach is one of prospective methods in controlling the flexible arms.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.65-68
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• 1996

Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. The TMS32OC31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the, network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time, control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of Ocean Engineering and Technology
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• v.3 no.2
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• pp.559-559
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• 1989

This paper deals with the robust controller design of robot manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach follwed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with the assumption that process is characterized by a linear model remaining time invariant during adaptation process. The performance of controller is demonstrated by computed simulation about position and speed control of six link manipulator in case of disturbance and payload variation.

• Journal of Ocean Engineering and Technology
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• v.3 no.2
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• pp.59-69
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• 1989

This paper deals with the robust controller design of robot manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach follwed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with the assumption that process is characterized by a linear model remaining time invariant during adaptation process. The performance of controller is demonstrated by computed simulation about position and speed control of six link manipulator in case of disturbance and payload variation.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.810-815
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• 2010

This paper proposes a vision/DR integrated navigation system using distance between wheels of the vehicle and a gyroscope. In order to show the validity of the proposed vision/DR integrated navigation system, experiments were performed for a trajectory of a mobile robot. Experimental results show that the proposed vision/DR integrated navigation system gives better navigation performance than a vision/DR integrated navigation system using only distance between wheels of the vehicle.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1995.03a
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• pp.131-144
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• 1995

This paper presetns modifications of a hydraulic shovel to robotize, and we derive a dynamic model of the hydraulic shovel arms,and hydraulic analysis are discussed . Then , our purpose is making to imitate a target railroad line of the grinder position by the PID control. Moreover, to determine the gains of the PId controller, we referenced the Ziegler and Nichols' method. In this paper, we demonstrated that the PID control is available for system. These results indicated the possibility of practical use fo the deburring robot with the hydraulic shovel.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.7-12
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• 1990

In this paper, we propose a new manipulator control scheme based on the CMAG neural network. The proposed control consists of two components. The feedforward component is an output of trained CMAC neural network and the feedback component is a modified sliding mode control. The CMAC accepts the position, velocity and acceleration of manipulator as input and outputs two values for the controller : One is the nominal torque used for feedforward compensation(M1 network) and the other is the inertia matrix related information used for the feedback component(M2 network). Since the used control algorithm guarantees the robust trajectory tracking in spite of modeling errors, the CMAC mapping errors due to the memory limitation are little worth consideration.