• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.10
• /
• pp.51-57
• /
• 1999

In this paper, we deal with the problem of controlling an unknown nonlinear dynamical system, using wavelet network. Accurate control of the nonlinear systems depends critically on the accuracy and efficiency of the function approximator used to approximate the function. Thus, we use wavelet network which shows high capability of approximating the functions and includes the free-selection of basis functions for the control of the nonlinear system. We find the dilation and translation that are wavelet network parameters by analyzing the time-frequency characteristics of the controller's input to construct an initial adaptive wavelet network controller. Then, weights is adjusted by the adaptive law based on the Lyapunov stability theory. We apply this direct adaptive wavelet network controller to control the inverted pendulum system which is an nonlinear system.

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

• Nuclear Engineering and Technology
• /
• v.25 no.1
• /
• pp.110-124
• /
• 1993

A Nonlinear model-based Hybrid Controller (NHC) is developed which consists of the adaptive proportional-integral-feedforward (PIF) gains and variable structure controller. The controller has the robustness against modeling uncertainty and is applied to the trajectory tracking control of single-input, single-output nonlinear systems. The essence of the scheme is to divide the control into four different terms. Namely, the adaptive P-I-F gains and variable structure controller are used to accomplish the specific control actions by each terms. The robustness of the controller is guaranteed by the feedback of estimated uncertainty and the performance specification given by the adaptation of PIF gains using the second method of Lyapunov. The variable structure controller is incorporated to regulate the initial peak of the tracking error during the parameter adaptation is not settled yet. The newly developed NHC method is applied to the power tracking control of a nuclear reactor and the simulation results show great improvement in tracking performance compared with the conventional model-based control methods.

• Proceedings of the KIEE Conference
• /
• 2008.10b
• /
• pp.443-444
• /
• 2008

본 논문에서는 비행기 모델을 대상으로 invariance and immersion방법을 적용하여 비선형 제어기를 설계한다. 이 방법을 사용하면 추정오차에 대한 추정식 형태로 off-the-manifold 좌표축을 정의하고, dynamics를 구하고, 이로부터 파라미터 적응규칙을 유도한다. 제어기는 이와 관련된 파라미터로부터 선형 화하지 않고 직접 설정하며, 마지막으로 리아프노프 함수를 통하여 알고리즘의 안정성을 증명한다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.2
• /
• pp.276-282
• /
• 2006

PID controller has been widely used in industry. Because it has a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose an adaptive PID controller based on a gradient descent learning. This algorithm has a simple structure like conventional PID controller and a robustness to system parameters variation and different velocity command. To verify performances of the proposed adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

• Proceedings of the KIEE Conference
• /
• 1997.07a
• /
• pp.115-117
• /
• 1997

본 논문에서는 유도전동기의 적응 비선형 제어에 대해 논한다. 제어 목적은 회전자저항의 불확실성에도 불구하고 유도전동기의 속도 및 자속을 분리하여 제어하는 것이며, 이를 위해 백스테핑(Backstepping) 기법을 사용한 비선형제어기와 회전자 저항 추정기를 결합한다. 제안된 적응제어시스템은 내부의 모든 변수가 유계(Bounded)되어 있다는 점에서 안정하며, 더불어 그 개선된 성능을 모의실험을 통해 보인다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.12
• /
• pp.2365-2370
• /
• 2008

In this paper, applications of wavelet neural network controller to position control of nonlinear system are considered. Wavelet neural network is used in the objectives which improve the efficiency of LQR controllers. It is possible to make unstable nonlinear systems stable by using LQR(Linear Quadratic Regulator) technique. And, in order to be adapted to disturbance effectively in this system it uses wavelet neural network controller. Applying this method to the position control of nonlinear system, its usefulness is verified from the results of experiment.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.40 no.6
• /
• pp.69-75
• /
• 2003

This paper presents a fuzzy logic based approach to tracking control of a sampled nonlinear system. It is assumed that the plant to be controlled is under both the internal uncertainty and the external disturbances. Discrete-time adaptive fuzzy control method is proposed and its parameters are determined by the recently-spolighted convex optimization technique called LMI. Finally, the computer simulation is tarried out to verify the effectiveness of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.12
• /
• pp.1925-1930
• /
• 2010

For high-speed/high-accuracy position control of a gantry-moving-type linear motor, we propose a nonlinear adaptive controller including a synchronization algorithm. Linear motors are easily affected by force ripple, friction, and parameter variations because there is no mechanical transmission to reduce the effects of model uncertainties and external disturbances. Synchronization error is also caused by skew motion, model uncertainties, and force disturbance on each axis. Nonlinear effects such as friction and ripple force are estimated and compensated for. The synchronization algorithm is used to reduce the synchronous error of the two side pillars. The performance of the controller is evaluated via computer simulations.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.215-218
• /
• 1997

A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.