• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.35 no.11
• /
• pp.990-998
• /
• 2007

Adaptive output feedback control technique using Neural Networks(NN) is proposed for uncertain nonlinear Multi-Input Multi-Output(MIMO) systems. Modified Dynamic Inversion Model(MDIM) is introduced to decouple uncertain nonlinearities from inversion-based control input. MDIM consists of approximated dynamic inversion model and inversion model error. One NN is applied to compensate the MDIM of the system. The output of the NN augments the tracking controller which is based upon a filtered error approximation with online weight adaptation laws which are derived from Lyapunov's direct method to guarantee tracking performance and ultimate boundedness. Several numerical results are illustrated in the simulation of Van der Pol system and unmanned helicopter with model uncertainties.

• KIEE International Transactions on Electrophysics and Applications
• /
• v.4C no.3
• /
• pp.96-100
• /
• 2004

The Nd： YAG laser is commonly used throughout many fields such as accurate material processing, IC marking, semiconductor annealing, medical operation devices, etc., due to the fact that it has good thermal and mechanical properties and is easy to maintain. In materials processing, it is essential to vary the laser power density for specific materials. The laser power density can be mainly controlled by the current pulse width and pulse repetition rate. It is important to control the laser energy in those fields using a pulsed laser. In this paper we propose the constant-frequency current resonant half-bridge converter and monitoring of capacitor charging voltage. This laser power supply is designed and fabricated to have less switching loss, compact size, isolation with primary and secondary transformers, and detection of capacitor charging voltage. Also, the output stabilization characteristics of this Nd： YAG laser system are investigated. The test results are described as a function of laser output energy and flashlamp arc discharging constant. At the energy storage capacitor charges constant voltage, the laser output power is 2.3％ error range in 600[V].

• Journal of Electrical Engineering and Technology
• /
• v.13 no.5
• /
• pp.1874-1885
• /
• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.3 s.168
• /
• pp.52-60
• /
• 2005

This paper deals with the position control fur a feed drive system in CNC milling machine, which utilizes a modified error signal for the elimination of steady-state error. A linear time-invariant (LTI) system has consistent properties in response to standard test signal inputs. Those also appear in an error curve acquired from the response. From such properties, constructed is an error model for the position control of the feed drive. And then added is the output of the error model to the current error signal. Consequently the resulting proportional control system brings performance improvement in view of the steady-state error. The effectiveness of the proposed scheme is confirmed through simulations and experiments.

• Journal of IKEEE
• /
• v.13 no.1
• /
• pp.11-18
• /
• 2009

In this paper, the stability of second order uncertain systems with regulation of PID type controllers is analyzed by using Lyapunov second method for the first time in the time domain. The property of the stability of PID regulation servo systems is revealed in sense of Lyapunov, i.e., bounded stability due to the disturbances and uncertainties. By means of the results of this stability analysis, the maximum norm bound of the error from the output without variation of the uncertainties and disturbances is determined as a function of the gains of the PID control, which make it enable to analyze the effect resulted from the variations of the disturbances and uncertainties using this norm bound for given PID gains. Using the relationship of the error from the output without variation of the uncertainties and disturbances and the PID gain with maximum bounds of the disturbances and uncertainties, the robust gain design rule is suggested so that the error from the output without the variation of the disturbances and uncertainties can be guaranteed by the prescribed specifications as the advantages of this study. The usefulness of the proposed algorithm is verified through an illustrative example.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.4
• /
• pp.154-157
• /
• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Journal of Satellite, Information and Communications
• /
• v.10 no.1
• /
• pp.22-28
• /
• 2015

In this paper, we propose a new computationally efficient joint iterative multi-input multi-output (MIMO) detection scheme using a soft interference cancellation and minimum mean squared-error (SIC-MMSE) method. The critical computational burden of the SIC-MMSE scheme lies in the multiple inverse operations of the complex matrices. We find a new way which requires only a single matrix inversion by utilizing the Taylor series expansion of the matrix, and thus the computational complexity can be reduced. The computational complexity reduction increases as the number of antennas is increased. The simulation results show that our method produces almost the same performances as the conventional SIC-MMSE with reduced computational complexity.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.41 no.3
• /
• pp.39-46
• /
• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for the speed control of interior permanent magnet synchronous motor(IPMSM) drive. The design of this algorithm based on FNN controller that is implemented by using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights among the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strongly high performance and robustness in parameter variation, steady-state accuracy and transient response.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.11
• /
• pp.650-656
• /
• 2003

A high speed Viterbi decoder with a circularly connected 2-dimensional analog processing ceil array Is proposed. The proposed Viterbi .decoder has a 2-dimensional parallel processing structure in which an analog processing cell is placed at each node of a trellis diagram, the output column of the analog processing cells is connected to the decoding column, and thus, the output(last) column becomes a column right before the decoding(first) column. The reference input signal given at a decoding column is propagated to the whole network while Its magnitude is reduced by the amount of a error metric on each branch. The circuit-based decoding is done by adding a trigger signals of same magnitudes to disconnect the path corresponding to logic 0 (or 1) and by observing its effect at an output column (the former column of the decoding column). The proposed Viterbi decoder has advantages in that it is operated with better performance of error correction, has a shorter latency and requires no path memories. The performance of error correction with the proposed Viterbi decoder is tested via the software simulation.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.1248-1250
• /
• 2004

This paper is proposed adaptive fuzzy-neural network controller(NFC) for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on NFC that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive NFC is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.