• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.59.3-59
• /
• 2002

$\textbullet$ Contents 1: Preliminaries on passivity and feedback passivity $\textbullet$ Contents 2: Assumptions in the paper $\textbullet$ Contents 3: Adaptive output feedback controllers $\textbullet$ Contents 4: Adaptive disturbance attenuation $\textbullet$ Contents 5: Adaptive passification $\textbullet$ Contents 6: An illustrative numerical example

• Transactions on Control, Automation and Systems Engineering
• /
• v.1 no.1
• /
• pp.1-7
• /
• 1999

This paper gives a simulation study of a new fuzzy logic control(FLC) approach for the mold level control in continuous casting processes. The proposed FLC is PID type hybridizing the conventional fuzzy PI control and Fuzzy PD control with a simplified design scheme. It is shown that, compared with the conventional control, this new control strategy can achieve superior performance for steady-state response and is more robust against process parameter variations and disturbances.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.2
• /
• pp.178-183
• /
• 2003

This work is a modified version of an earlier work that was based on ellipsoidal type feasible sets. Unlike the earlier work, polyhedral types of invariant and feasible sets are adopted to deal with input constraints. The use of polyhedral sets enables the formulation of on-line algorithm in terms of QP (Quadratic Programming), which can be solved more efficiently than semi-def algorithms. A simple numerical example shows that the proposed method yields larger stabilizable sets with greater bounds on disturbances than is the case in the earlier approach.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.321-325
• /
• 1986

A new control scheme for the state space model of the robot system using the theory of optimal multi-variable structure is presented in this paper. It is proposed to optimize multi-dimensional variable structure systems for obtaining the required stabilizing signal by minimizing a performance index with respect to the state vector in the sliding mode. It is concluded the proposed variable structure controller yields better system dynamic performance than that obtained by using the only linear optimal controller inthat responses for a step disturbance have a shorter setting time, no matter what overshoot values and rising time.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.373-376
• /
• 1986

This paper proposes a robust model following control system which realizes good properties such as asymptotic stability, disturbance rejection and model following with reduced sensitivity for plant parameter variation. This algorithm can be easily applied to the multivariable control systems and the control structure is simple. As an example the aircraft control system of a convair C-131B is designed and its characteristics are examined by simulation.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.2
• /
• pp.136-139
• /
• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10a
• /
• pp.101-106
• /
• 1990

The problem of constructing an observer for use in the control of superheater temperature with desuperheater is considered. The distributed heat input into the superheater is usually not available for use in the observer, and hence is treated as a disturbance. The observer theory for systems with unknown inputs is exploited and applied to the problem. Approximation of the heat input utilizing the specific heat input distribution pattern is also considered.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.200-200
• /
• 2000

Highly nonlinear dynamical systems are easily identified using neural networks. When disturbances are included in the learning data set Int system modeling, modeling process will be poorly performed. Since the radial basis functions in the radial basis function network(RBFN) are centered at the points specified by the weights, RBF networks are robust for approximating the process including the narrow-band disturbances deviating significantly from the regular signals. To exclude(filter) these disturbances, a robust algorithm for system identification, based on the RBFN, is proposed. The performance of system identification excluding disturbances is investigated and compared with the one including disturbances.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1996.10a
• /
• pp.172-175
• /
• 1996

In the paper is proposed a hierarchical self-learning fuzzy controller for balancing and position control of an circular inverted pendulum system. To stabilize the pendulum at a specified position, the hierarchical fuzzy controller consists of a supervisory controller, a self-learning fuzzy controller, and a forced disturbance generator. Simulation example shows the effectiveness of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.10
• /
• pp.1-11
• /
• 1996

In this paper, a robust receding horizon control algorithm, which can be employed for a wide class of nonlinear systems with control and state constraints, modeling errors, and disturbances, is considered. In a neighborhood of the origin, a linear feedback controlelr for the linearized system is applied. Outside this neighborhood, a receding horizon control is applied. Robust stability is proved considering the time taken to solve an optimal control problem so that the proposed algorithm can be applied as an on-line controller.