• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.37-47
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• 2023

This paper introduced how to construct Denavit-Hartenberg (DH) parameters from the Unified Robot Description Format (URDF). URDF is convenient for describing a robot even though the robot is very complex. On the other hand, DH convention is not an easy notation for many novices who want to describe a robot. Therefore, most vendors provide URDF and users prefer to use URDF to describe a robot. However, some controllers or algorithms are based on DH parameters to perform kinematics, dynamics, control, etc. To connect URDF and DH parameters, we present a three-step approach to construct DH parameters from URDF. The first step is to define the joint axis for constructing DH parameters. The second step is constructing DH parameters to define joint character. The final step is constructing DH parameters to define the coordinate frame of the child link. This approach is based on intuitive vector calculation and guarantees the uniqueness of DH parameters. To verify our approach, we applied our approach to a simple one-link robot, a manipulator with 6 DOF, and a quadruped robot with 3 DOF per leg. We verified that our approach worked well based on forward kinematic results.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.296-301
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• 2005

This paper describes the kinematics, dynamics and control of a 6-DOF shaking table with a bell crank structure, which converts the direction of reciprocating movements. In this shaking table, the bell crank mechanism is used to reduce the amount of space needed to install the shaking table and create horizontal displacement of the platform. In kinematics, joint design is performed using $Gr{\ddot{u}}bler's$ formula. The inverse kinematics of the shaking table is discussed. The derivation of the Jacobian matrix is presented to evaluate singularity conditions. Considering the maximum stroke of the hydraulic actuator, collision between links and singularity, workspace is computed. In dynamics, computations are based on the Newton-Euler formulation. To derive parallel algorithms, each of the contact forces is decomposed into one acting in the direction of the leg and the other acting in the plane orthogonal to the direction of the leg. Applying the Newton-Euler approach, the solution of inverse dynamics is almost completely parallel. Only one of the steps-the application of the Newton-Euler equations to the platform-must be performed on one single processor. Finally, the efficient control scheme is proposed for the tracking control of the motion platform.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.11
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• pp.753-763
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• 2015

This paper proposes a decentralized robust adaptive neural network control scheme using multiple radial basis function neural networks for electrically driven robot manipulators with bounded input voltages in the presence of uncertainties. The proposed controller considers both robot link dynamics and actuator dynamics. Practically, the controller gain coefficients applied at each joint may be nonlinear time-varying and the input voltage at each joint is saturated. The proposed robot controller overcomes the various uncertainties and the input voltage saturation problem. The proposed controller does not require any robot and actuator parameters. The adaptation laws of the proposed controller are derived by using the Lyapunov stability analysis and the stability of the closed-loop control system is guaranteed. The validity and robustness of the proposed control scheme are verified through simulation results.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.8
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• pp.1203-1211
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• 2009

The dynamics of underwater vehicles can be greatly influenced by the dynamics of the vehicle thrusters. The control of the state of the hovering or very slow motion of the underwater vehicle is most important for automatic docking or control of the manipulator of the vehicle. The dynamics of the thruster based on the electric motor is nonlinear and has uncertain parameters. Since the dynamics of the vehicle coupled with the dynamics of the thruster is nonlinear and has uncertain parameters, a robust control is very effective for a desired motion tracking of the uncertain and nonlinear vehicle. In this paper a study was performed on the robust control scheme of the very slow motion or hovering motion of the underwater vehicle actuated by the electric motor. Also, a concurrent control on the state of the vehicle with nonlinearity and uncertain parameters was performed. A sliding mode control algorithm out of robust controllers was designed and applied, which compensates the nonlinear forces and uncertain parameters of the vehicle and actuator. Through a computer simulation, the proposed control scheme was compared with a linear PD controller and its superior performance was validated.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.389-395
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• 2006

This paper proposes the controller for biped robot using intelligent control algorithm. In order to simplify the complexity of biped robot control, manipulator of biped robot is divided into four modules. These modules are controlled by intelligent algorithm with Hierarchical Mixture of Experts(HME) using neural network. Also neural network having direct control method learns the inverse dynamics of biped robot. The HME, which is a network of tree structure, reallocates the input domain for the output by learning pattern of input and output. In this paper, as a result of learning HME repeatedly with EM algorithm, the controller for biped robot operating safety walking is designed by modelling dynamics of biped robot and generating virtual error of HME.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.5 no.4
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• pp.15-20
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• 1991

The paper considers a distributed adaptive control technique for industrial robots which contribute to the factory automation. The control object is to tracking for a desired trajectories under various load conditions., rapidly against load variation these control techniques divided whole system into subsystems which is controlled with the nominal and adaptation controllers, and also the asymptotic stability of these substem was proved. Simulation results shown that the proposed techniques was feasible in spite of nonlinear dynamics of robot manipulator and payload variations.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1256-1263
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• 2004

Output feedback passivation problem is studied when the given system is not minimum-phase or does not have relative degree one. Using a parallel connection with an additional dynamics, the authors provide a dynamic output feedback control law which renders the composite system passive. Sufficient conditions are presented under which the composite system is output feedback passive. As an application of the dynamic passivation scheme, a point-to-point control law for a flexible joint robot is presented when only the position measurements are available. This provides an alternative way of replacing the role of the velocity measurements for the proportional-derivative (PD) feedback law. The performance of the proposed control law is illustrated in the simulation studies of a manipulator with three revolute elastic joints.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1853-1858
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• 2004

This paper presents the development of a full order sliding mode controller for tracking problem of a class of uncertain dynamical system, in particular, the direct drive robot manipulators. By treating the arm as an uncertain system represented by its nominal and bounded parametric uncertainties, a new robust fullorder sliding mode tracking controller is derived such that the actual trajectory tracks the desired trajectory as closely as possible despite the non-linearities and input couplings present in the system. A proportional-integral sliding surface is chosen to ensure the stability of overall dynamics during the entire period i.e. the reaching phase and the sliding phase. Application to a three DOF direct drive robot manipulator is considered.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.36-44
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• 1993

In the development of a high speed and light weight manipulator, it is necessary to consider the flexibility of a robotic arm. The infinite dynamics must be analyzed to obtain the finite mode modeling to achieve the feasible controller design of the robotic arm. The modeling procedures of the flexible robot arm, and natural frequencies and mode shapes by the constrained and unconstrained mode method are illustrated. The transfer function of the robot arm with a payload is also shown. The controller is designed by the pole assignment and optimal control theory to compensate for the unmodelled dynamic effects to the low order system. Also, the pole assignment method involving the harmonic vibration mode is presented through computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.