• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.1478-1481
• /
• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• 제어로봇시스템학회논문지
• /
• 제11권6호
• /
• pp.524-529
• /
• 2005

In this paper, a novel neural network compensation technique for PD like fuzzy controlled robot manipulators is presented. A standard PD-like fuzzy controller is designed and used as a main controller for controlling robot manipulators. A neural network controller is added to the reference trajectories to modify input error space so that the system is robust to any change in system parameter variations. It forms a neural-fuzzy control structure and used to compensate for nonlinear effects. The ultimate goal is same as that of the neuro-fuzzy control structure, but this proposed technique modifies the input error not the fuzzy rules. The proposed scheme is tested to control the position of the 3 degrees-of-freedom rotary robot manipulator. Performances are compared with that of other neural network control structure known as the feedback error learning structure that compensates at the control input level.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2003년도 추계종합학술대회
• /
• pp.945-948
• /
• 2003

본 논문에서는 시스템에 발생하는 외란의 영향을 최소로 하기 위해 다층 신경회로망을 이용한 강인 PID 제어 방식을 제안한다. 제안한 방식은 제어 시스템에 발생하는 불특정의 외란을 필터링 하기 위해 다층 신경망을 이용한다. 외란이 포함된 실제 플랜트의 입력을 신경망의 출력으로 보상하여 플랜트를 순수하게 PID 제어기로만 제어하도록 한다. DC 모터에 대한 시뮬레이션과 MM-LDM(Moving Magnetic Linear DC Motor)의 위치제어 실험을 통해 제안한 알고리즘의 유용성을 확인하였다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
• /
• pp.903-905
• /
• 2003

In this paper, adaptive high-order neural network controller(AHONNC) is adopted to control of an induction servomotor. A algorithm is developed by combining compensation control and high-order neural networks. Moreover, an adaptive bound estimation algorithm was proposed to estimate the bound of approximation error. The weight of the high-order neural network can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the closed-loop system can be guaranteed. Simulation results for induction servomotor drive system are shown to confirm the validity of the proposed controller.

• 제어로봇시스템학회논문지
• /
• 제10권2호
• /
• pp.112-124
• /
• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• 제어로봇시스템학회논문지
• /
• 제7권8호
• /
• pp.647-655
• /
• 2001

In adaptive neuro-control, neural networks are used to approximate unknown plant nonlinearities. Until now, most of the studies in the field of controller design for nonlinear system using neural network considers the affine system with fixed number of neurons. This paper considers nonaffine nonlinear systems and on-line variation of the number of neurons. A control law and adaptive laws for neural network weights are established so that the whole system is stable in the sense of Lyapunov. In addition, at the expense of th input, tracking error converges to the arbitrary small neighborhood of the origin. The efficiency of the proposed scheme is shown through simulations ofa simple nonaffine nonlinear system.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
• /
• pp.36-39
• /
• 1996

This paper proposes identification and control algorithm of nonlinear systems and the proposed fuzzy-neural network has following characteristics. The network is roughly divided into premise and consequence. The consequence function is nonlinear function which consists of three parameters and the membership function in the premise contains of two parameters. The parameters in premise and consequence are learned by the extended back-propagation algorithm which has a modified form of the generalized delta rule. Simulation results on the identification show that this method is more effective than that of Narendra [3]. The indirect fuzzy-neural control is made of the fuzzy-neural identification and controller. Result on the indirect fuzzy-neural control shows that the proposed fuzzy-neural network can be efficiently applied to nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
• /
• pp.411-415
• /
• 1994

An adaptive learning control scheme by use of multilayer neural networks for compensating for uncertainties in nonlinear dynamic system is examined. Multilayer neural networks are introduced to map the uncertainties in nonlinear dynamics and perform nonlinear state feedback. Parameters of neural networks are adjusted by conventional back-propagation algorithms modified with the projection operation. Effectiveness of the proposed scheme for tracking control are demonstrated through computer simulations.

• 대한전기학회논문지:전력기술부문A
• /
• 제48권3호
• /
• pp.286-293
• /
• 1999

A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer-based torque estimator with low pass filter should dbjust the time constant of the adopted filter according to the natural resonance frequency detemined by considering the system parameters varied. The simulation results shows the validity of the proposed control scheme.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.131-134
• /
• 1997

This paper presents a new approach to the design of intelligent control system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.