• 조명전기설비학회논문지
• /
• 제12권4호
• /
• pp.122-127
• /
• 1998

산업 자동화의 고정밀도에 따라 직류 전통기는 강인제어가 요구되고 었다. 하지만 전동기 제어 시스템이 부하 외란의 영향을 받게되면 강인제어는 어렵게 된다. 슬라이딩 모드 제어는 강인성올 갖지만, 강인성을 갖는 슬라이딩 모드 제어에서의 불연속 제어법칙은 원하지 않는 떨림 현상이 발생한다. 이를 해결하기 위한 한 방법으 로 본 논문에서는 전동기 제어 시스템을 위한 신경망 슬라이딩 모드 제어기법올 제시하였다. 제의된 제어기는 떨립 현상 없이 부하 외란을 효과적으로 제거할 수 있었다. 제어기법의 효과는 시뮬레이션에 의해 확인하였다.

• 한국산학기술학회논문지
• /
• 제9권5호
• /
• pp.1178-1183
• /
• 2008

슬라이딩 모드를 가진 가변 구조 제어(VSC)는 비선형 시스템의 현대제어에서 중요하고 흥미로운 주제이다. 그러나, VSC에서의 불연속적인 제어 법칙은 실제로 바람직하지 못한 떨림 현상을 발생시킨다. 본 논문에서는 이러한 문제점을 해결하기 위해 신경망 슬라이딩 곡면을 갖는 VSC 구조를 제안한다. 불연속 제어 법칙을 해결하기 위해 경계층을 가진 신경망 슬라이딩 곡면이 도입된다. 제안된 제어기는 보편적인 VSC의 떨림 현상 문제를 해결할 수 있다. 제안된 제어 구조의 효과는 시뮬레이션을 통해 증명하였다.

• 한국정밀공학회지
• /
• 제25권12호
• /
• pp.89-99
• /
• 2008

In this paper, the position tracking control problem of the servo system with nonlinear dynamic friction is issued. The nonlinear dynamic friction contains a directly immeasurable friction state variable and the uncertainty caused by incomplete parameter modeling and its variations. In order to provide the efficient solution to these control problems, we propose the composite control scheme, which consists of the robust friction state observer, the FNN approximator and the approximation error estimator with sliding mode control. In first, the sliding mode controller and the robust friction state observer is designed to estimate the unknown internal state of the LuGre friction model. Next, the FNN estimator is adopted to approximate the unknown lumped friction uncertainty. Finally, the adaptive approximation error estimator is designed to compensate the approximation error of the FNN estimator. Some simulations and experiments on the servo system assembled with ball-screw and DC servo motor are presented. Results show the remarkable performance of the proposed control scheme. The robust friction state observer can successfully identify immeasurable friction state and the FNN estimator and adaptive approximation error estimator give the robustness to the proposed control scheme against the uncertainty of the friction parameters.

• 드라이브 ㆍ 컨트롤
• /
• 제19권1호
• /
• pp.51-61
• /
• 2022

This paper presents a model-free system based on a framework of a backstepping sliding mode control (BSMC) with a radial basis function neural network (RBFNN) and adaptive mechanism for electro-hydraulic systems (EHSs). First, an EHS mathematical model was dedicatedly derived to understand the system behavior. Based on the system structure, BSMC was employed to satisfy the output performance. Due to the highly nonlinear characteristics and the presence of parametric uncertainties, a model-free approximator based on an RBFNN was developed to compensate for the EHS dynamics, thus addressing the difficulty in the requirement of system information. Adaptive laws based on the actor-critic neural network (ACNN) were implemented to suppress the existing error in the approximation and satisfy system qualification. The stability of the closed-loop system was theoretically proven by the Lyapunov function. To evaluate the effectiveness of the proposed algorithm, proportional-integrated-derivative (PID) and improved PID with ACNN (ACPID), which are considered two complete model-free methods, and adaptive backstepping sliding mode control, considered an ideal model-based method with the same adaptive laws, were used as two benchmark control strategies in a comparative simulation. The simulated results validated the superiority of the proposed algorithm in achieving nearly the same performance as the ideal adaptive BSMC.

• 한국군사과학기술학회지
• /
• 제4권2호
• /
• pp.30-41
• /
• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, It needs a robust performance which can get over the nonlinear characteristics due to hull shape. Second, It needs an accurate performance which has the small overshoot phenomenon and steady state error to avoid colliding with ground surface and obstacles. Third, It needs a continuous control input to reduce the acoustic noise. Finally, It needs an effective interpolation method which can reduce the dependency of control parameters on speed. To solve these problems, we propose a Intelligence depth control method using Fuzzy Sliding Mode Controller and Neural Network Interpolator. Simulation results show the proposed control scheme has robust and accurate performance by continuous control input and has no speed dependency problem.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.176.2-176
• /
• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, it needs robust performance which can get over nonlinear characteristics. Second, it needs accurate performance which have small overshoot phenomenon and steady state error. Third, it needs continuous control input. Finally, it needs interpolation method which can solve the speed dependency problem of controller parameters. To solve these problems, we propose adepth control method using Fuzzy Sliding Mode Controller and Neural Network Interpolator. Simulation results show the proposed method has robust and accurate control performance by the continuous control input and has no speed dependency problem.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2008년도 추계학술대회 논문집
• /
• pp.127-128
• /
• 2008

• 전자공학회논문지B
• /
• 제33B권5호
• /
• pp.1-9
• /
• 1996

In this paper, a parameter identification method for a discrete-time linear system using multi-layer neural network is proposed. The parameters are identified with the combination of weights and the output of neuraons of a neural network, which can be used for a linear and a nonlinear controller. An adaptive output tracking architecture is designed for the linear controller. And, the nonlinear controller. A sliding mode control law is applied to the stabilizing the nonlinear controller such that output errors can be reduced. The effectiveness of the proposed control scheme is illustrated through simulations.

• 한국조명전기설비학회:학술대회논문집
• /
• 한국조명전기설비학회 1998년도 학술발표회논문집
• /
• pp.212-216
• /
• 1998

• 제어로봇시스템학회논문지
• /
• 제14권10호
• /
• pp.1014-1021
• /
• 2008

This paper proposes an intelligent sliding mode control method for robotic systems with the unknown bound of model uncertainties. In our control structure, the unknown bound of model uncertainties is used as the gain of the sliding controller. Then, we employ the function approximation technique to estimate the unknown nonlinear function including the width of boundary layer and the uncertainty bound of robotic systems. The adaptation laws for all parameters of the self-recurrent wavelet neural network and those for the reconstruction error compensator are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with model uncertainties and external disturbances. Accordingly, the proposed method can not only overcome the chattering phenomenon in the control effort but also have the robustness regardless of model uncertainties and external disturbances. Finally, simulation results for the five-link biped robot are included to illustrate the effectiveness of the proposed method.