• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.301-306
• /
• 2003

PID controller is applied mostly to two-order system. In third-order or higher- system, it's impossible to get high response quality because of having more zero point than the number of zero point being in the PID controller. To solve those, Jung & Dorf suggested a new type of PIDA controller and solved problen of a third-order system. But, as the result of getting step response using PIDA controller, rising time is very quickly but wide overshoot is happened. Beside designing PIDA controller with using CDM(Coefficient Diagram Method) suggested by shunji manabe. But, In Performance standard, CDM decreases overshoot to desired but rising time is very slow. Therefore this paper suggest a PD-PIDA controller for low overshoot with PD type Pre-compensator. This paper applied designed PD-PIDA controller to position control of 3-Phase induction motor.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10b
• /
• pp.401-405
• /
• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.1 s.94
• /
• pp.200-209
• /
• 1999

This paper presents a precise tracking control scheme for the proposed parallel robot using artificial neural network. This control scheme is composed of three feedback controllers and one feedforward controller. Conventional PD controller and artificial neural network are used as feedback and feedforward controller respectively. A backpropagation learning strategy is applied to the training of artificial neural network, and PD controller outputs are used as target outputs. The PD controllers are designed at the robot dynamics based on inter-relationship between active joints and moving platform. Feedback controllers insure the total stability of system, and feedforward controller generates the control signal for trajectory tracking. The precise tracking performance of proposed control scheme is proved by computer simulation.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.703-707
• /
• 1994

This paper proposes a method of learning control in DC servomotor using a neural network. First we estimate the pulse transfer function of the servo system with an unknown load, then we determine the best gains of I-PD control system using a neural network. Each time the load changes, its best gains of the I-PD control system is computed by the neural network. And the best gains and its pulse transfer function for the case are stored in the memory. According the increase of the set of gains and its pulse transfer function, the learning control system can afford the most suitable I-PD gains instantly.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.180.5-180
• /
• 2001

This paper presents A Design of WMR Controller that being composed of cooperative relation between PID controller and optimized neural network algorithm, it operate a variable control by velocity. Some proposed algorithm in the past just depended on PID controller for the control of position of WMR but for more efficient control we design a variable controller that operate control by PD controller using neural network if it is satisfied with any given condition. it adjust gain of PD controller for real time control using a fast feedforward algorithm which is different with Form of the standard backpropagation algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.21 no.10
• /
• pp.1619-1625
• /
• 1997

This paper concerns a 2-axis PR type pneumatic manipulator system translating in vertical and rotating in horizontal directions. A simplified linear model is mathematically formulated similar to the pneumatic acturators in dynamic responses in order to devise an appropriate position control scheme. A PD controller preceding the on/off solenoid valve turns out not only economical but also effective in reducing rise time and amplitude of limit cycles, if its control gains are determined on the basis of frequency response. And, additional implementation of symmetric or asymmetric deadband at the PD controller output greatly helps minimize valve opening numbers, positional error, and undesirable direction-dependent property due to the gravitational load. Such a control concept is synthesized through numerical simulations and next applied to the experimental set-up, featuring enhanced positional servo characteristics.

• 제어로봇시스템학회:학술대회논문집
• /
• 1999.10a
• /
• pp.21-24
• /
• 1999

There-have been many control strategies for the enact joint position tracking of flexible manipulators, but direct cartesian space tracking control methods an not developed well. In this paper, we propose a PD control method based on the cartesian error in the end point trajectory tracking. the proposed controller is composed of PD control combined with nonlinear saturation term hut has a very simple form. the effect of this term is continuous suppression of vibration which is induced by the coupling of rigid motion. This control works both on the regulation and on the tracking cases. The performance and validity of this control method is shown by simulation examples.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.28 no.5
• /
• pp.603-609
• /
• 2004

This paper deals with the issue of Neural Network-based control for a rodless pneumatic cylinder system which is utilized for a pneumatic XY-plotter. In order to identify the system design parameters, the open loop response of a pneumatic rodless cylinder controlled by a pneumatic servovalve is investigated by applying a self-excited oscillation method. Based on the system design parameters, the PD feedback compensator is designed and then Neural Network is incorporated with it. The experiment of a trajectory tracking control using a PD-NN has been performed and proved its excellent performance by comparing with that of a PD feedback compensator.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2089-2091
• /
• 2001

In this paper, a hybrid controller that consists of a conventional PD controller and a neural network controller which adapts to various control conditions by online learning is used and a new learning algorithm of the neural networks is used to prevent weights of neural network from diverging. A conventional PI controller and the hybrid controller is applied to speed control of 3 phase induction motor. So in comparison with a PD controller, we prove superiority of hybrid controller by experiments.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.67.1-67
• /
• 2001

This paper presents a neural network based self-tuning fuzzy PI-PD control scheme for robust speed control of induction motor. The PID controller is being widely used in industrial applications. When continuously used long time, the electric and mechanical parameters of induction motor change, degrading the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, and proposes a neural network based self-tuning fuzzy PI-PD controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small ...