• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.41 no.4
• /
• pp.21-28
• /
• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.39 no.6
• /
• pp.1-8
• /
• 2002

This paper presents a generalized minimum-variance self-tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior and time delays. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation is done to adapt the nonlinear nonminimum phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct adaptive controller using neural network.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.20 no.6
• /
• pp.570-579
• /
• 2009

In this paper, we presented a Vt-DRO with a wide electrical frequency tuning range, using open-loop gain method. The Vt-DRO was composed of 3-stages, resonator, amplifier and phase shifter. In order to satisfy an oscillation condition, we determined magnitude and phase of each stage. The measured S-parameter of cascaded 3-stages shows open-loop oscillation condition. Also, using measured open loop group delay, we derived the relation for electrical frequency tuning range. The Vt-DRO was implemented by connecting the input and the output of the designed open-loop and resulted in closed-loop. As a results, tuning-range of Vt-DRO is 82 MHz, which is close to the predicted results for tuning voltage 0${\sim}$10 V and shows linear frequency tuning at the center frequency of 5.3 GHz. The phase noise is -104 ${\pm}$1 dBc/Hz at 100 kHz offset frequency and power is 5.86${\pm}$1 dBm respectively.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.19 no.2
• /
• pp.114-123
• /
• 2005

A nonlinear speed control of a PMSM using a sequential parameter auto-tuning algorithm for servo equipments is presented. The nonlinear control scheme gives an undesirable output performance under the mismatch of the system parameters and load conditions. Recently, to improve the performance, an adaptive linearization scheme, a sliding mode control and an observer-based technique have been reported. Although a good performance can be obtained, the performance is not satisfactory any more under specific conditions such as a large inertia variation, a fast speed transient or an increased sampling time. The simultaneous estimation of principal parameters giving a direct influence on speed dynamics is generally not simple. To overcome this problem, a a sequential parameter auto-tuning algorithm at start-up is proposed, where dominant parameters are estimated in a prescribed regular sequence based on the method that one parameter is estimated during each interval. The proposed scheme is implemented on a PMSM using DSP TMS320C31 and the effectiveness is verified through simulations and experiments.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.5
• /
• pp.605-614
• /
• 2005

In this paper, we present the design and implementation of an agent knowledge and QoS tuning methodology to improve the flexibility of agent-based flexible video-conference system. In order to improve the flexibility during video-conferencing, we propose a new T-INTER(Tuning-INTER) architecture of knowledge part in video-conference manager (VCM) agent in which an automatic QoS parameter tuning method is imbedded. The flexible video-conference system structured based on the proposed architecture can cope with the changes in service quality required by users. The VCM agent cooperates with other agents by protocols and executes the automatic QoS parameter tuning task whenever needed. By the tuned parameters, the system is able to flexibly cope with the internal or external changes and the burden of users can be decreased. In the experimental section, it is shown that our proposed system outperforms the existing system.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.4
• /
• pp.364-370
• /
• 1990

In this paper, we propose a self-tuning PID controller for unknown systems with time-varying delay. Using pole placement equations, we derive the controller that can be extended to the multi-step time delay case. The time-varying delays are estimated by a prediction error delay method using multiple predictors. Since the order of the estimation vector is not increased, the persistant exciting condition of control input is alleviated. Since the least square method gives biased parameter estimates for colored noise cases, the recursive instrumental variable method is used to estimate system parameters. The computational burden of the proposed method is less than the conventional adaptive methods. Computer simulations are performed to illustrate the efficiency of the proposed method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.2542-2547
• /
• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.6
• /
• pp.915-923
• /
• 2008

This paper focuses on optimal tuning of nonlinear parameters of a dual-input power system stabilizer(dual-input PSS), which can improve the system damping performance immediately following a large disturbance. Until recently, various PSS models have developed to bring stability and reliability to power systems, and some of these models are used in industry applications. However, due to non-smooth nonlinearities from the interaction between linear parameters(gains and time constants of linear controllers) and nonlinear parameters(saturation output limits), the output limit parameters cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures('trial and error' approach) have been used. Therefore, the steepest descent method is applied to implement the optimal tuning of the nonlinear parameters of the dual-input PSS. The gradient required in this optimization technique can be computed from trajectory sensitivities in hybrid system modeling with the differential-algebraic-impulsive-switched(DAIS) structure. The optimal output limits of the dual-input PSS are evaluated by time-domain simulation in both a single machine infinite bus(SMIB) system and a multi-machine power system in comparison with those of a single-input PSS.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.38 no.1
• /
• pp.139-152
• /
• 2013

The purpose of this study is to claim the validity of tuning the architecture of neural network models for multi-class classification. A neural network model for multi-class classification is basically constructed by building a series of neural network models for binary classification. Building a neural network model, we are required to set the values of parameters such as number of hidden nodes and weight decay parameter in advance, which draws special attention as the performance of the model can be quite different by the values of the parameters. For better performance of the model, it is absolutely necessary to have a prior process of tuning the parameters every time the neural network model is built. Nonetheless, previous studies have not mentioned the necessity of the tuning process or proved its validity. In this study, we claim that we should tune the parameters every time we build the neural network model for multi-class classification. Through empirical analysis using wine data, we show that the performance of the model with the tuned parameters is superior to those of untuned models.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.61-64
• /
• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using Digital signal processors for robot manipulators. TMS32OC50 is used in implementing real-time adaptive control algorithms to provide advanced performance for robot manipulator. In this paper, an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. Parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm, and controller parameters are determined by the pole-placement method. Performance of self-tuning adaptive controller is illustrated by the simulation and experiment for a SCARA robot.