• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.44 no.4
• /
• pp.447-452
• /
• 1995

In this paper a simple method of a position control for brushless DC motor is presented. For precise position control, a high performance torque controller is needed and a novel current control method is proposed. The current controller detects the uncommutating mode current for every 60.deg. (elec. angle) and controls it with PI controller. The current control loop includes the feedforward of back EMF and the feedforward of the neutral voltage between the neutral point of the inverter and the neutral point of the machine. In the position control, the acceleration pattern is calculated from the position reference. Then the speed trajectory is calculated from the acceleration pattern. The experimental results are presented to verify the proposed methods.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.1022-1027
• /
• 1991

A new method to control a robot manipulator by neural networks is proposed. The controller is composed of both a PD controller and a neural network-based feedforward controller. MLP(multi-layer perceptron) neural network is used for the feedforward controller and trained by BP(back-propagation) learning rule. Error terms for BP learning rule are composed of the outputs of a PD controller and the acceleration errors of manipulator joints. We compare the proposed method with existing ones and contrast performances of them by simulation. Also, We discuss the real application of the proposed method in consideration of the learning time of the neural network and the time required for sensing the joint acceleration.

• Proceedings of the KIPE Conference
• /
• 1998.07a
• /
• pp.352-356
• /
• 1998

This paper is a study about Ac servo motor position and speed control characteristics which depend on feedforward control, the acceleration / deceleration time constant after the interpolation, and PI control, automatic deceleration at corner in order to shape cutting control of feed drive system of the machine tool. The shape error caused by delay of the servo system in the direction of radius at the time of circular cutting is reduced by feedforward control. The shape error generated by the position command delay is minimized by using the acceleration / deceleration time constant after the interpolation. The results were verified to optical machining center experimentation of the machine tool.

• Proceedings of the KIPE Conference
• /
• 1997.07a
• /
• pp.336-340
• /
• 1997

This paper is a study about motor technic of motion and feedforward control in order to shape cutting control on the machine tool. The shape error caused by delay of the servo system in the direction of radius at the time of circular cutting is reduced by feedforward control, shape error generated by the position command delay is minimized by using the acceleration/deceleration time constant after the interpolation. The study was verified to optimization of motion control on experiments of a vertical machining center of the machine tool.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.2
• /
• pp.74-82
• /
• 2004

In this paper, a disturbance feedforward compensator design technique is proposed for an active magnetic bearing system subject to base motion for attenuating disturbance responses. In the consideration of the requirements on the model accuracy in the model based compensator designs, an experimental feedforward compensator design based on adaptive estimation by means of the Multiple Filtered-x least mean square(MFXLMS) algorithm is proposed. The performance and the effectiveness of the proposed technique will be presented in the succeeding paper in which the proposed technique is applied to a 2-DOF active magnetic bearing system subject to base motion.

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

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.2
• /
• pp.83-91
• /
• 2004

This paper illustrates the feasibility and the effectiveness of the disturbance feedforward compensation control proposed in the previous paper. The compensator is designed experimentally by means of the Multiple Filtered-x Least Mean Square algorithm. A 2-DOF active magnetic bearing system subject to base motion is built and the compensation control is applied. The experimental results demonstrate that the compensation control reduces the air-gap responses within 10$%$ of those by the feedback control alone without increasing the control inputs.

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

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.11
• /
• pp.31-35
• /
• 1992

This paper presents a decentralized adaptive controller design for a robot manipulator to track the given desired trajectory in the joint space. The controller is of distributed structure and does not require the complex manipulator dynamic model, thereby it is computationally very efficient. Each joint is independently controlled by a PID feedback part and a velocity-acceleration feedforward part. Simulation results for a two-link direct drive manipulator conform that the proposed decentralized scheme is feasible.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.1194-1198
• /
• 1993

Active roll control can improve handling and ride comfort. Dynamic characteristics of the hydraulic actuators for active suspension, which can be modeled as the 1'st order time lag system, hinders the performance improvement. To overcome this shortcoming a predictive controller is designed based on 3 d.o.f. linear vehicle handling model. The effect of this controller is studied through the simulation based on 10 d.o.f. nonlinear vehicle model and the results is compared to that of feedforward controller which uses lateral acceleration as control signal.