• Journal of Power Electronics
• /
• v.16 no.2
• /
• pp.598-609
• /
• 2016

The good control performance of permanent magnet linear synchronous motor (LSM) drive systems is difficult to achieve using linear controllers because of uncertainty effects, such as fictitious forces. A backstepping control system using adaptive modified recurrent Laguerre orthogonal polynomial neural network uncertainty observer (OPNNUO) is proposed to increase the robustness of LSM drive systems. First, a field-oriented mechanism is applied to formulate a dynamic equation for an LSM drive system. Second, a backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. As the LSM drive system is prone to nonlinear and time-varying uncertainties, an adaptive modified recurrent Laguerre OPNNUO is proposed to estimate lumped uncertainties and thereby enhance the robustness of the LSM drive system. The on-line parameter training methodology of the modified recurrent Laguerre OPNN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre OPNN are derived to accelerate parameter convergence. Finally, the effectiveness of the proposed control system is verified by experimental results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.12 no.4
• /
• pp.122-127
• /
• 1998

Robust control for DC motor is needed according to the highest precision of industrial automation. However, when a motor control system has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. The sliding mode control has robustness, but the discontinuous control law in sliding mode control with robustness leads to undesirable chattering in practice. As a method solving this problem, in this paper, neural network sliding mod control method for motor control system is presented. The proposed controller effectively can eliminate load disturbance without chattering. The effectiveness of the control scheme is verified by simulation results.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.8
• /
• pp.617-622
• /
• 2000

In this paper proposed is an $H_{\ifty}$ based flow controller for the ATM networks. The round trip time-delay uncertainty is taken into account and robustness of the proposed controller is analyzed. Maximum allowable time-delay uncertainties are computed with different weightings on performance and robustness. And discussed is a time-domain implementation method of the proposed controller. Time domain simulation with realistic environment demonstrates that the performance of the proposed controller is much better than that of conventional one.

• Journal of the Korea Society for Simulation
• /
• v.1 no.1
• /
• pp.48-54
• /
• 1992

This study discusses the performance evaluation of queueing network methodologies as used for the planning of FMS production systems. The possibility of applications and utilities of queueing network models is investigated for FMS producton plans. Experimental results by queueing network models such as CAN-Q, MVAQ and results by detailed simulation models written in SIMAN are compared and some propositions are presented based on the results of the experiments.

• ETRI Journal
• /
• v.31 no.4
• /
• pp.399-407
• /
• 2009

In this paper, we present a robust $H_{\infty}$ distributed power control scheme for wireless CDMA communication systems. The proposed scheme is obtained by optimizing an objective function consisting of the user's performance degradation and the network interference, and it enables a user to address various user-centric and network-centric objectives by updating power in either a greedy or energy efficient manner. The control law is fully distributed in the sense that only its own channel variation needs to be estimated for each user. The proposed scheme is robust to channel fading due to the immediate decision of the power allocation of the next time step based on the estimations from the $H_{\infty}$ filter. Simulation results demonstrate the robustness of the scheme to the uncertainties of the channel and the excellent performance and versatility of the scheme with users adapting transmit power either in a user-centric or a network-centric efficient manner.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.171-171
• /
• 2023

This study investigates the capability of Physics-Informed Neural Networks (PINNs) for solving the solution of partial differential equations. Particularly, the 1D Saint-Venant Equations (SVEs) were considered, which describe the movement of water in a domain with shallow depth compared to its horizontal extent, and are widely adopted in hydrodynamics, river, and coastal engineering. The core contribution of this work is to combine the robustness of neural networks with the physical constraints of the SVEs. The PINNs method utilized a neural network to approximate the solutions of SVEs, while also enforcing the underlying physical principles of the equations. This allows for a more effective and reliable solution, especially in areas with complex geometry and varying bathymetry. To validate the robustness of the PINNs method, numerical experiments were conducted on several benchmark problems. The results show that the PINNs could be achieved high accuracy when compared with the solution from the numerical solution. Overall, this study demonstrates the potential of using PINNs and highlights the benefits of integrating neural network and physics information for improved efficiency and accuracy in solving SVEs.

• Journal of KIISE:Information Networking
• /
• v.35 no.6
• /
• pp.510-520
• /
• 2008

These days, the network convergence for utilizing heterogeneous network on mobile device is being very actively studied. However, understanding characteristics of physical network interfaces and video encoder is needed for using the network convergence technologies efficiently. Thus, this paper proposes an optimized method for streaming video data through different network paths depending on data characteristics and channel condition. Accordingly, unlike the traditional methods, this study divides scalable coded videos by layer importance, the importance of stream information, and the importance in consideration of video decoder's robustness and selectively sends the data via multiple channels. And the experimental results show over 1dB increment in PSNR. The result of this study will provide an optimized video transmission technique in the next generation network convergence environment in which mobile devices have multiple network interfaces.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1997.10a
• /
• pp.181-184
• /
• 1997

Practical applications of learning systems usually involve complex domains exhibiting nonlinear behavior and dilution by noise. Consequently, an intelligent system must be able to adapt to nonlinear processes as well as probabilistic phenomena. An important class of application for a knowledge based systems in prediction: forecasting the future trajectory of a process as well as the consequences of any decision made by e system. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes in the form of chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a Henon process in the presence of various patterns of noise.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.23 no.1
• /
• pp.109-141
• /
• 1998

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

• Proceedings of the KIEE Conference
• /
• 1999.07c
• /
• pp.1177-1179
• /
• 1999

This paper presents a design of artificial neural network power system stabilizer(ANNPSS) using adaptive evolutionary algorithm(AEA). We have proposed an adaptive evolutionary algorithm which uses both a genetic algorithm(GA) and an evolution strategy(ES), useing the merits of two different evolutionary computations. ANNPSS shows better control performances than conventional power system stabilizer(CPSS) in three-phase fault with heavy load which is used when tuning ANNPSS. To show the robustness of the proposed ANNPSS, it is applied to damp the low frequency oscillation caused by disturbances such as three-phase fault with normal and light load. the proposed ANNPSS shows better robustness than CPSS.