• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10b
• /
• pp.558-561
• /
• 1993

The main difference between a linear system and a nonlinear system is the existence of direct interactions between input signals. These interactions will be classified into three types, (1) self-interaction among different order terms of control signals, (2) static mutual interactions between the control signals, and (3) dynamic interactions through the coefficient venctor fields of the control variables. In this paper, we will show that interactions of type (2) and (3) can be avoided by applying an appropriate dynamic compensator, while the interaction of type (1) is fatal.

• Proceedings of the KIEE Conference
• /
• 1988.11a
• /
• pp.36-41
• /
• 1988

Analog and Digital Speed Control Systems have mutually complementary properties. Analog System has good dynamic characteristics and moderate steady-state accuracy and can be implemented economically with operational a ampliers. Digital System, on the contray, has good static accuracy, but relatively poor dynamic property. So, a hybrid system which uses both digital and analog control can have good static and dynamic characteristics. In this paper, it is shown that a simple digital controller can improve steady-state accuracy of existing analog control system satisfactorily, and some design criteria are presented also.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.156-161
• /
• 1994

In this paper, we consider an optimal control problem of a nonlinear stochastic system. Dynamic programming approach is employed for the formulation of a stochastic optimal control problem. As an optimality condition, dynamic programming equation so called the Bellman equation is obtained, which seldom yields an analytical solution, even very difficult to solve numerically. We obtain the numerical solution of the Bellman equation using an algorithm based on the finite difference approximation and the contraction mapping method. Optimal controls are constructed through the solution process of the Bellman equation. We also construct a test case in order to investigate the actual performance of the algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.275-278
• /
• 1995

In this paper, we consider a dynamic shape control problem with an example of controlling a flexible beam shape. Mathematical formulations are obtained by employing the Green's function approach. Necessary conditions for optimality are derived by considering the quadratic performance criteria. Numerical results for both of the dynamic and the static cases are obtained and compared.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.652-657
• /
• 1987

The Dynamic Matrix Control(DMC) technique was applied to nonlinear and nonminimum phase system. System model was identified by using Least Square method. Desired output trajectory was prespecified and input suppression parameter was also introduced. It was shown that DMC technique worked with great success in solving both nonminimum phase system and nonlinear system.

• Proceedings of the KIEE Conference
• /
• 1998.11b
• /
• pp.454-456
• /
• 1998

Traditional adaptive control algorithms are not robust to dynamic uncertainties. The adaptive control algorithms developed previously to deal with dynamic uncertainties do not facilitate quantitative design. We proposed a new robust adaptive control algorithms consists of an $H^{\infty}$ suboptimal control law and a robust parameter estimator. Numerical examples showing the effectiveness of the $H^{\infty}$ adaptive scheme are provided.

• Journal of the Korea Safety Management & Science
• /
• v.7 no.5
• /
• pp.213-231
• /
• 2005

Assignable causes producing temporary deviation from the underlying system can influence on process adjustment and process monitoring in dynamic feedback control system. In this paper, the influence of assignable causes on EWMA forecasts and compensatory variables are derived for a dynamic feedback control system. An example is presented to confirm the impact numerically through the analysis of a data.

• Smart Structures and Systems
• /
• v.11 no.3
• /
• pp.315-329
• /
• 2013

The construction of an experimental nonlinear structural model with little cost and unlimited repeatability for vibration control study represents a challenging task, especially for material nonlinearity. This paper reports the design, analysis and vibration control of a nonlinear structural experiment platform with adjustable hinges. In our approach, magnetorheological rotary brakes are substituted for the joints of a frame structure to simulate the nonlinear material behaviors of plastic hinges. For vibration control, a separate magnetorheological damper was employed to provide semi-active damping force to the nonlinear structure. A dynamic neural network was designed as a state observer to enable the feedback based semi-active vibration control. Based on the dynamic neural network observer, an adaptive fuzzy sliding mode based output control was developed for the magnetorheological damper to suppress the vibrations of the structure. The performance of the intelligent control algorithm was studied by subjecting the structure to shake table experiments. Experimental results show that the magnetorheological rotary brake can simulate the nonlinearity of the structural model with good repeatability. Moreover, different nonlinear behaviors can be achieved by controlling the input voltage of magnetorheological rotary damper. Different levels of nonlinearity in the vibration response of the structure can be achieved with the above adaptive fuzzy sliding mode control algorithm using a dynamic neural network observer.

• Journal of the Korean Society of Industry Convergence
• /
• v.19 no.2
• /
• pp.81-87
• /
• 2016

This paper presents a new approach to control of stable motion of single wheel driving robot system of a pitch that is controlled by an in-wheel motor and a roll that is controlled by a reaction wheel. This robot doesn'thave any actuator for a yaw axis control, which makes the derivation of the dynamics relatively simple. The Lagrange equations was applied to derive the dynamic equations of the one wheel driving robot to implement the dynamic speed control of the mobile robot. To achieve the real time speed control of the unicycle robot, the sliding mode control and optical regulator are utilized to prove the reliability while maintaining the desired speed tracking performance. In the roll controller, the sigmoid-function based robust controller has been adopted to reduce the vibration by the situation function. The optimal controller has been implemented for the pitch control to drive the unicycle robot to follow the desired velocity trajectory in real time using the state variables of pitch angle, angular velocity, angle and angular velocity of the driving wheel. The control performance of the control systems from a single dynamic model has been illustrated by the real experiments.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.4
• /
• pp.309-314
• /
• 2007

This paper presents an adaptive neural computation algorithm for multi-layered neural networks which are applied to identify the characteristic function of dynamic systems. The main feature of the proposed algorithm is that the initial learning rate for the employed neural network is assigned systematically, and also the assigned learning rate can be adjusted empirically for effective neural leaning. By employing the approach, enhanced modeling of dynamic systems is possible. The effectiveness of this approach is veri tied by simulations.