• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.335-341
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• 2010

To improve the performance of inner loop based on PD controller for a unmanned helicopter, neural networks are applied. The performance of PD controller designed on the response characteristics of error dynamics decreases because of uncertain nonlinearities of the system. The nonlinearities are decoupled to modified dynamic inversion model(MDIM) and are compensated by the neural networks. For the training of the neural networks, online weight adaptation laws which are derived from Lyapunov's direct method are used to guarantee the stability of the controller. The results of the improved performance of PD controller by neural networks are illustrated in the simulation of unmanned helicopter with nonlinearities,

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.3
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• pp.525-531
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• 2001

The paper demonstrates that RBF(Radial Basis Function) networks can be used effective for the identification of inverted pendulum system. With the parallel arrangement of the RBF networks controller and PD controller, some characteristics were compared through simulation performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.157-165
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• 2002

This paper presents the implementation of the speed control system for 3 phase induction motor using PD controller and neural networks. The PD controller is used to control the motor and to train neural networks at the first time. And neural networks are widely used as controllers because of a nonlinear mapping capability, we used feedforward neural networks(FNN) in order to simply design the speed control system of the 3 phase induction motor. Neural networks are tuned online using the speed reference, actual speed measured from an encoder and control input current to motor. PD controller and neural networks are applied to the speed control system for 3 phase induction motor, are compared with PI controller through computer simulation and experiment respectively. The results are illustrated that the output of the PD controller is decreased and feedforward neural networks act main controller, and the proposed hybrid controllers show better performance than the PI controller in abrupt load variation and the precise control is possible because the steady state error can be minimized by training neural networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.375-382
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• 2004

This paper presents control designs using neural networks (NN) for a XY positioning table. The proposed neuro-controller is composed of an outer PD tracking loop for stabilization of the fast flexible-mode dynamics and an NN inner loop used to compensate for the system nonlinearities. A tuning algorithm is given for the NN weights, so that the NN compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded weight estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The proposed neuro-controller is implemented and tested on an IBM PC-based XY positioning table, and is applicable to many precision XY tables. The algorithm, simulation, and experimental results are described. The experimental results are shown to be superior to those of conventional control.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.5
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• pp.46-55
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• 2000

This paper presents a direct adaptive control of robot system using chaotic neural networks and PD controller. The chaotic neural networks have robust nonlinear dynamic characteristics because of the sufficient nonlinearity in neuron itself, and the additional self-feedback and inter-connecting weights between neurons in same layer. Since the structure and the learning method are not appropriate for applying in control system, this neural networks have not been applied. In this paper, a modified chaotic neural networks is presented for dynamic control system. To evaluate the performance of the proposed neural networks, these networks are applied to the trajectory control of the three-axis PUMA robot. The structure of controller consists of PD controller and chaotic neural networks in parallel for conforming the stability in initial learning phase. Therefore, the chaotic neural network controller acts as a compensating controller of PD controller.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1679-1687
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• 2016

Mobile robots are effectively used in industrial fields that require flexible manufacturing systems. Mobile robots have to move with mechanical loads such as product parts along the specified paths, and are usually equipped with kinematic controllers. When the loads and nonlinear frictions are too high, satisfactory control performances can not be expected with the kinematic controllers, so some dynamic controllers have been developed. Conventional dynamic controllers require the exact weights and locations of the loads; however, the loads are frequently changed and unknown so that the control performances of the conventional controllers are limited. This paper proposes an adaptive PD control method using gradient descent learning to have sufficient dynamic control performance for unknown loads. Simulation studies have been conducted for various load conditions to verify that the adaptive PD control method have much broader convergence region than the convention method.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.281-285
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• 2013

In this paper, a design scheme of a cruise control system for an electric vehicle using piece-wised PD control is proposed. Cruise control of electric vehicles is one of the major performance elements. Drive motors having linear characteristics ideally is required in order to achieve the cruise driving. But practical motors have nonlinear characteristics and the performance of the motors can be improved by the closed-loop control to compensate it. In this paper, we improved the performance of by applying piece-wised PD control because the driving motors having nonlinear characteristics are difficult to obtain adequate performance only using closed-loop control. In order to test the proposed method, the experiments were carried out by applying the proposed method after setting up an electric vehicle equiped with a driving motors having large nonlinear characteristics. The experiment results of the proposed piece-wised PD control shows better performance than that of closed-loop control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.166-172
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• 1998

PD 제어기는 산업현장에 가장 널리 운영되는 제어알고리즘이지만 지금까지 보편적으로 사용되고 있는 PID 파라메터 동조방법인 Ziegler-Nichols 동조법으로는 화학공정 같은 비선형 특성이 크거나, 시정수가 큰 플래트에서는 좋은 성능을 얻을 수 없다. 본 논문에서는 릴레이 동조 실험을 통하여 임계 이득과 발진주기를 구하고, 이 값들로부터 Z-N 동조법을 적용하여 초기 동조값을 구한다. 이 값에 의해서 얻어진 공정 출력의 오차와 오차변화율을 입력으로 한 퍼지 동조기를 통하여 PID 제어기의 비례이득과 적분시간을 구하는 동조 방법을 제시한다.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.1
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• pp.105-110
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• 2017

This paper introduces a heave compensation system for offshore crane when it subjected to unexpected disturbances such as ocean waves, tidal currents or winds and their external force. The dynamic model consists of a crane which is considered to behave in the same manner as a rigid body, a hydraulic driven winch, an elastic rope and a payload. To keep the payload from moving upwards and downwards, PD(Proportional-Derivative) control was applied by using linearization. In order to achieve a better performance, the sliding mode control and the nonlinear generalized predictive control algorithm was applied according to the time-delay. As a result, the oscillating amplitude of the payload was reduced by the control algorithm. Considering the time-delay involved in the system to be one second, nonlinear generalized predictive controller with a robust controller was a suitable control algorithm for this heave compensation system because it made the position of te payload reach the desired position with the minimum error. This paper presented a control algorithm using the robust control and its simulation results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.4
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• pp.40-46
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• 2006

It is required to develop a highly reliable attitude & orbit control system of satellite that is less expensive as the technology of satellite design & integration is recently matured dramatically. To accomodate this kind of needs, the two axis attitude control method for wheel-based momentum-biased satellite system whose momentum bias vector points to a certain direction(sun direction), is developed using simple but reliable sensors and actuator: three axis magnetometer and coarse sun sensor are used as sensors, and magnetic torque bars are used as actuator. Classical PD type controller design methodologies are applied on a satellite system for the two axis control with the proper assumptions. Nonlinear simulation results are included to demonstrate the long term stability and the performance of closed-loop system design results.