• Ocean Systems Engineering
• /
• v.1 no.3
• /
• pp.249-261
• /
• 2011

The stochastic nonlinear dynamic behavior and probability density function of ship rolling are studied using the nonlinear dynamical systems approach and probability theory. The probability density function of the rolling response is evaluated through solving the Fokker Planck Equation using the path integral method based on a Gauss-Legendre interpolation scheme. The time-dependent probability of ship rolling restricted to within the safe domain is provided and capsizing is investigated from the probability point of view. The random differential equation of ships' rolling motion is established considering the nonlinear damping, nonlinear restoring moment, white noise and colored noise wave excitation.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.1045-1050
• /
• 1992

A new sliding surface for a viaraible structure control(VSC) law is emplyed to achieve fast and robust path tracking in a class of second-order nonlinear unceratin dynamical systems. The surface onitialy passes arbitrarily given initial conditions and subsequently moves towards a predetermined surface via rotaiting or/and shifting. We call it as a moving sliding surface(MSS). The surface is then incorporated with the VSC law which is constructed by imposing the sliding conditions in a special way. We primarily enforce the conditions which assume that once the system state is on a sliding surface that it is driven towards the zero state. Using the VSC law associatied with the MSS, it is shown that the tracking behavoirs are remarkably improved in the sene of the fastness and the robustness.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.6
• /
• pp.705-713
• /
• 1999

In this paper, we propose a new motion and force control methodology for constrained robots as an approach of hybrid discrete-continuous dynamical system. The hybrid dynamic system modeling of robotic manipulation tasks with constraints is presented, and the hybrid system control architecture for unconstrained and constrained motion system with parametric uncertainties is synthesized. The optimal reference stiffness of robot manipulator is generated by the hybrid automata as a discrete state system and the control behavior of constrained system which has poor modeling information and time-varying constraint function is improved by the constrained robots as a continuous state system. The performance of the proposed constrained motion control system is successfully evaluated via experimental studies to the constraint tasks.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.11
• /
• pp.1104-1109
• /
• 2010

This paper deals with the attitude control of an underactuated spacecraft that has fewer than three actuators. Even though such spacecrafts are known as uncontrollable, restricted missions are possible with controlling two-axis attitude angles. A variable speed control moment gyroscope is considered as an actuator. It is a kind of momentum exchange device and it shows highly nonlinear dynamical properties. Speed commands are generated by kinematic equations represented by Euler angles. A control law, that is designed to make a spacecraft follow the speed commands, is derived by the backstepping method. Angular speeds are estimated from the attitude measurements. Several estimation methods have been compared.

• Journal of The Korean Astronomical Society
• /
• v.40 no.4
• /
• pp.91-97
• /
• 2007

The Fokker-Planck (FP) model is one of the commonly used methods for studies of the dynamical evolution of dense spherical stellar systems such as globular clusters and galactic nuclei. The FP model is numerically stable in most cases, but we find that it encounters numerical difficulties rather often when the effects of tidal shocks are included in two-dimensional (energy and angular momentum space) version of the FP model or when the initial condition is extreme (e.g., a very large cluster mass and a small cluster radius). To avoid such a problem, we have developed a new integration scheme for a two-dimensional FP equation by adopting an Alternating Direction Implicit (ADI) method given in the Douglas-Rachford split form. We find that our ADI method reduces the computing time by a factor of ${\sim}2$ compared to the fully implicit method, and resolves problems of numerical instability.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.401-403
• /
• 1998

This paper describes the neural network control method for the identification and control of chaotic nonlinear dynamical systems effectively. In our control method, the controlled system is modeled by an unknown NARMA model, and a feedforward neural network is used for identifying the chaotic system. The control signals are directly obtained by minimizing the difference between a setpoint and the output of the neural network model. Since learning algorithm guarantees that the output of the neural network model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the setpoint.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.11a
• /
• pp.120-123
• /
• 1997

In this paper, we propose the Fuzzy Inference-based Reinforcement Learning Algorithm. We offer more similar learning scheme to the psychological learning of the higher animal's including human, by using Fuzzy Inference in Reinforcement Learning. The proposed method follows the way linguistic and conceptional expression have an effect on human's behavior by reasoning reinforcement based on fuzzy rule. The intervals of fuzzy membership functions are found optimally by genetic algorithms. And using Recurrent state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying to the inverted pendulum control problem.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.6
• /
• pp.616-628
• /
• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.14 no.4
• /
• pp.322-331
• /
• 2014

Chaotic dynamics is an active area of research in biology, physics, sociology, psychology, physiology, and engineering. This interest in chaos is also expanding to the social scientific fields such as politics, economics, and argument of prediction of societal events. In this paper, we propose a dynamic model for addiction of tobacco. A proposed dynamical model originates from the dynamics of tobacco use, recovery, and relapse. In order to make an addiction model of tobacco, we try to modify and rescale the existing tobacco and Lorenz models. Using these models, we can derive a new tobacco addiction model. Finally, we obtain periodic motion, quasi-periodic motion, quasi-chaotic motion, and chaotic motion from the addiction model of tobacco that we established. We say that periodic motion and quasi-periodic motion are related to the pre-addiction or recovery stage, respectively. Quasi-chaotic and chaotic motion are related to the addiction stage and relapse stage, respectively.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.10
• /
• pp.1037-1043
• /
• 2011

Two-wheeled balancing mobile robots are currently controlled in terms of linear control methods without considering the nonlinear dynamical characteristics. However, in the high maneuvering situations such as fast turn and abrupt start and stop, such neglected terms become dominant and greatly influence the overall driving performance. This paper addresses the SDRE nonlinear optimal control method to take advantage of the exact nonlinear dynamics of the balancing robot. Simulation results indicate that the SDRE control outperforms LQR in the respect of transient performance and required wheel torques. A design example is suggested for the state matrix that provides design flexibility in the SDRE control. It is shown that a well-planned state matrix by reflecting the physics of a balancing robot greatly contributes to the driving performance and stability.