In this paper, the goal is to obtain a dynamic model of a particular system. The system is a combination of a wheeled vehicle(chassis) with a turret rotating in azimuth direction and a gun rotating in a elevation direction. At this time, the motion of the gun according to the shaking of the continuous shot is obtained using the coordinate transformation equation in the azimuth and elevation angle. Also, the dynamic model for the swaying of wheeled vehicle is obtained through the Lagrange’s equation. Through this, we analyze the tumbles of the gun, whiat is the major term, and what dynamics are needed for stabilization control.

Mordern Ocean-going ships utilize stabilization techniques in order to minimize the effects of oscillations due to the unwanted disturbances. In this paper, as an elementary design of automatic control system with linear-state vari;tble feedback and series compensator for ship stabilization, analysis and design is limited to the linear time-invariant single input and output system. In order for the Controlled system to meet the requirements of stability, accuracy and transient response, a model of the automatic control system is proposed. For the analysis and design of this model, the state-space method, that is, the mordern way, or an alternative to the transfer function method of describing a linear system that utilize the state variables and state equations, is applied.

컨테이너 화물의 복합운송시스템 중에서 체화현상은 항만에서 가장 심각하다. 이러한 문제를 해결하기 위해 해상과 육상의 경계선에서 체선체화 문제를 발생시키는 컨테이너 크레인의 직업효율을 향상시키는 방법을 생각할 수 있다. 이를 위해 본 연구에서는 가능한 범위에서 트롤리를 목표지점까지 빠르게 이동시키는 동시에 목표위치에서의 흔들림도 짧은 시간 내에 제어하는 문제를 다루고 있다. 제어 전략으로 설계된 구간에서 최적의 성능과 강인성이 보장되는 LQ 제어와 제약조건에서 최적화가 가능한 실수코딩 유전알고리즘을 결합한다. 컴퓨터 시뮬레이션을 통해 제안한 제어기가 설정한 설계사양을 완벽하게 만족하는 것을 보임으로 그 유효성을 증명한다.

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.