• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.4
• /
• pp.247-253
• /
• 2000

In this paper, we deal with the full range control problem of nonlinear boiler systems subject to complex actuator constraints. Firstly, $H\infty$ loop shaping design procedure［10］ is used for the controller design. Secondly, modified high-gain feedback［11］ for the loop shaping controller is adopted for the anti-windup function and the bumpless transfer technique between controllers is proposed for the full range control of nonlinear systems. Finally, the performance of the proposed controller is demonstrated through the simulation studies.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.7
• /
• pp.491-495
• /
• 2016

We deal with the regulation problem of nonlinear systems with time-varying delays in both the states and input. A new state feedback controller with dynamic gains is developed based on matrix inequality and non-predictor methods. The proposed control scheme is analyzed using the Razumikhin theorem, and its effectiveness is demonstrated with simulation results.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.2 no.3
• /
• pp.220-226
• /
• 2002

In this paper, we develop the intelligent digitally redesigned fuzzy controller for nonlinear systems. Takagi-Sugeno (TS) fuzzy model is used to model the nonlinear systems and a continuous-time fuzzy-model-based controller is designed based on the extended parallel-distributed-compensation(EPDC) method . The digital controllers are determined from existing analogue controllers. The proposed method provides an accurate and effective method for digital control of continuous-time nonlinear systems and enables us to efficiently implement a digital controller via the pre-determined continuous-time 75 fuzzy-model-based controller. We have applied the proposed method to the duffing forced oscillation system to show the effectiveness and feasibility of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.3
• /
• pp.65-71
• /
• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.444-444
• /
• 2000

For a class of nonlinear systems which satisfy a certain condition so called output feedback exponential passivity (OFEP), it is well known that one can easily design a high-gain output feedback control system. The designed high-gain controller has simple structure and high robustness. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper. we deal with a design problem of the robust high-gain adaptive output feedback control for the above-mentioned class of nonlinear systems with uncertain nonlinearities and/or disturbances.

• Transactions on Control, Automation and Systems Engineering
• /
• v.3 no.1
• /
• pp.21-26
• /
• 2001

In this study, we propose a two degree of freedom robust output tracking control method for a class of nonlinear system. We consider hyperbolically nonminimum phase single-input single-output uncertain nonlinear systems. We also consider the case that the nominal input-state equation is differentially flat. Nominal stable state trajectory is obtained in the flat output space via the flat output. Nominal feedforward control input is also computed from the nominal state trajectory. Due to the nature of the method, the generated flat output trajectory and control input are noncausal. Robust feedback control is designed to stabilize the systems around the nominal trajectory. A numerical example is given is given to demonstrate that robust tracking is achieved.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.6
• /
• pp.582-587
• /
• 1997

This paper deals with the control problem under nonlinear boiler systems with noise, and input constraints. MCMBPC(Multivariable Constrained Model-Based Predictive Controller) proposed by Wilkinson et al.[10,11] is used and nominal model is modified in this paper in order to applied to nonlinear boiler systems with feed-forward terms. The solution of the cost function optimization constrained on input and/or output variables is achieved using quadratic programming, via singular value decomposition(SVD). The controller designed is shown to satisfy the constraints and to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.9
• /
• pp.929-938
• /
• 2009

This paper presents a novel control methodology for communication network based nonlinear systems with time delay nature. We construct a nominal nonlinear control law for representing a linear model and a reset control system which is aimed for corrective control strategy to compensate system error due to uncertain time delay through wireless communication network. Next, online neural control approach is proposed for overcoming nonstationary statistical nature in the network topology. Additionally, DBN (Dynamic Bayesian Network) technique is accomplished for modeling of its dynamics in terms of casuality, which is then utilized for estimating prediction of system output. We evaluate superiority and reliability of the proposed control approach through numerical simulation example in which a nonlinear inverted pendulum model is employed as a networked control system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.183-186
• /
• 2002

In this paper, a predictive control method using wavelet neural network for chaotic nonlinear systems is presented. In our method, we use the adjusting method of the parameter for the training a wavelet neural network. The control signals are directly obtained by minimizing the difference between a reference signal and the output of a wavelet neural network. To verify the efficiency of our method, we apply it to the Duffing and the Henon system, which are a representative continuous and discrete time chaotic nonlinear system respectively.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.4
• /
• pp.696-705
• /
• 1997

This paper presents an efficient adaptive predistortion technique compensating linear and nonlinear distortions caused by high-power amplifier (HPA) with memory in OFDM systems. The efficient adaptive data predistortion techniques proposed for compensation of HPA with memory in single carrier systems cannot be applied to OFDM systems since the possible input levels for HPA is infinite in OFDM systems. Also, previous adaptive predistortion techniques, based on Volterra series modeling, are not suitable for real-time implementation due to high computational burden and slow convergence rate. In the proposed approach, the memoryless HPA preceded by a linear filter in OFDM systems is modeled by the Wiener system which is then precompensated by the proposed adaptive predistorter with a minimum number of filter taps. An adaptive algorithm for adjusting the proposed adaptive predistorter is derived using the stochastic gradient method. It is demonstrated by computer simulation that the performance of OFDM system suffering from nonlinear distortion can be greatly improved by the proposed efficient adaptive predistorter using a small number of filter taps.