• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.1
• /
• pp.19-26
• /
• 2005

This paper is concerned with the design of a predictive PID controller which has similar features to the model-based predictive controller. A PID type control structure is defined, which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are precalculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with the conventional PID and fuzzy control algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.7
• /
• pp.3480-3500
• /
• 2017

Accurate locating for the mobile target remains a challenge in various applications of wireless sensor networks (WSNs). Unfortunately, most of the typical localization algorithms perform well only in the WSN with densely distributed sensor nodes. The non-localizable problem is prone to happening when a target moves into the WSN with sparsely distributed sensor nodes. To solve this problem, we propose a collaborative and predictive localization algorithm (CPLA). The Gaussian mixture model (GMM) is introduced to predict the posterior trajectory for a mobile target by training its prior trajectory. In addition, the collaborative and predictive schemes are designed to solve the non-localizable problems in the two-anchor nodes locating, one-anchor node locating and non-anchor node locating situations. Simulation results prove that the CPLA exhibits higher localization accuracy than other tested predictive localization algorithms either in the WSN with sparsely distributed sensor nodes or in the WSN with densely distributed sensor nodes.

• Journal of Power Electronics
• /
• v.16 no.1
• /
• pp.277-286
• /
• 2016

This paper offers two Maximum Power Point Tracking (MPPT) systems for Photovoltaic (PV) applications. The first MPPT method is based on a fixed frequency Model Predictive Control (MPC). The second MPPT technique is based on the Predictive Hysteresis Control (PHC). An experimental demonstration shows that the proposed techniques are fast, accurate and robust in tracking the maximum power under different environmental conditions. A DC/DC converter with a high voltage gain is obligatory to track PV applications at the maximum power and to boost a low voltage to a higher voltage level. For this purpose, a high gain Switched Inductor Quadratic Boost Converter (SIQBC) for PV applications is presented in this paper. The proposed converter has a higher gain than the other transformerless topologies in the literature. It is shown that at a high gain the proposed SIQBC has moderate efficiency.

• International Journal of Safety
• /
• v.5 no.2
• /
• pp.6-11
• /
• 2006

The time delay in servo control on LonWorks/IP Virtual Device Network (VDN) is highly stochastic in nature. LonWorks/IP VDN induced time delay deteriorates the performance and stability of the real-time distributed control system and hinders an effective preventive and predictive maintenance. Especially in real-time distributed servo applications on the factory floor, timely response is essential for predictive and preventive maintenance. In order to guarantee the stability and performance of the system for effective preventive and predictive maintenance, LonWorks/IP VDN induced time delay needs to be predicted and compensated for. In this paper position control simulation of DC servo motor using Zero Phase Error Tracking Controller (ZPETC) as a feedforward controller, and Internal Model Controllers (IMC) based on Smith predictor with disturbance observer as a feedback controller is performed. The validity of the proposed control scheme is demonstrated by comparing the IMC based on Smith predictor with disturbance observer.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.4
• /
• pp.602-610
• /
• 2018

The model predictive control is an effective method to optimize the current control input that predicts the current control state and the future error using the predictive model of the control system when the reference trajectory is known. Since the control input can not have a physically infinitely large value, a predictive controller design with constraints should be considered. In addition, the reference model $A_r$ and the weight matrices Q, R that determine the control performance of the predictive controller are not optimized as arbitrarily designated should be considered in the controller design. In this study, we construct a predictive controller of a mobile robot by transforming it into a quadratic programming problem with constraints, The control performance of the mobile robot can be improved by optimizing the control parameters of the predictive controller that determines the control performance of the mobile robot using genetic algorithm. Through the computer simulation, the superiority of the proposed method is confirmed by comparing with the existing method.

• KIEAE Journal
• /
• v.15 no.4
• /
• pp.105-110
• /
• 2015

Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined.

• Journal of the FoodService Safety
• /
• v.2 no.2
• /
• pp.91-96
• /
• 2021

This study was performed to provide fundamental data on hygiene and quality control of ready-to-eat sandwiches. Predictive models were developed to the kinetics of Staphylococcus aureus growth in these sandwiches as a function of temperature (10, 15, 25, and 35℃). The result of the primary model that used the Gompertz equation showed that the lag phase duration (LPD) and generation time (GT) decreased and the exponential growth rate (EGR) increased with increasing storage temperature. The secondary model showed an R² for M and B of 0.9967 and 09916, respectively. A predictive growth model of the growth degree as a function of temperature was developed. L(t)=A+Cexp(-exp(-B(t-M))) (A=Initial contamination level, C=MPD-A, B=0.473166-0.045040*Temp-0.001718*Temp*Temp, M=19.924824-0.627442*Temp-0.004493*Temp*Temp, t=time, Temp=temperature). This model showed an R² value of 0.9288. All the models developed in this study showed a good fit.

• Journal of Internet Computing and Services
• /
• v.21 no.3
• /
• pp.83-92
• /
• 2020

Recently, many companies and organizations are interested in predictive process monitoring for the efficient operation of business process models. Traditional process monitoring focused on the elapsed execution state of a particular process instance. On the other hand, predictive process monitoring focuses on predicting the future execution status of a particular process instance. In this paper, we implement the function of the business process remaining time prediction, which is one of the predictive process monitoring functions. In order to effectively model the remaining time, normalization by activity is proposed and applied to the predictive model by taking into account the difference in the distribution of time feature values according to the properties of each activity. In order to demonstrate the superiority of the predictive performance of the proposed model in this paper, it is compared with previous studies through event log data of actual companies provided by 4TU.Centre for Research Data.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.33 no.11
• /
• pp.48-56
• /
• 2005

Predictive filter theory proposed recently can be characterized by inherent advantages of estimating modelling error and overcoming the disadvantage of the Kalman filter theory. A one-step ahead error is minimized to produce optimized filter performance in the form of the predictive filter. The main advantage of this filter lies in the ability to estimate both state vector and system model error. In this paper, attitude estimation results based upon the predictive filter theory is addressed. Mathematical formulation for estimating bias signal is peformed by using the predictive filter theory, and attitude estimation based upon vector observation is presented. From the results of this study, the potential applicability of the predictive filter is highlighted.

• International Journal of Highway Engineering
• /
• v.17 no.6
• /
• pp.11-18
• /
• 2015

PURPOSES : The purpose of this thesis is to evaluate the effectiveness of an active noise cancellation (ANC) system in reducing the traffic noise level against frequencies from the predictive model developed by previous research. The predictive model is based on ISO 9613-2 standards using the Noble close proximity (NCPX) method and the pass-by method. This means that the use of these standards is a powerful tool for analyzing the traffic noise level because of the strengths of these methods. Traffic noise analysis was performed based on digital signal processing (DSP) for detecting traffic noise with the pass-by method at the test site. METHODS : There are several analysis methods, which are generally divided into three different types, available to evaluate traffic noise predictive models. The first method uses the classification standard of 12 vehicle types. The second method is based on a standard of four vehicle types. The third method is founded on 5 types of vehicles, which are different from the types used by the second method. This means that the second method not only consolidates 12 vehicle types into only four types, but also that the results of the noise analysis of the total traffic volume are reflected in a comparison analysis of the three types of methods. The constant percent bandwidth (CPB) analysis was used to identify the properties of different frequencies in the frequency analysis. A-weighting was applied to the DSP and to the transformation process from analog to digital signal. The root mean squared error (RMSE) was applied to compare and evaluate the predictive model results of the three analysis methods. RESULTS : The result derived from the third method, based on the classification standard of 5 vehicle types, shows the smallest values of RMSE and max and min error. However, it does not have the reduction properties of a predictive model. To evaluate the predictive model of an ANC system, a reduction analysis of the total sound pressure level (TSPL), dB(A), was conducted. As a result, the analysis based on the third method has the smallest value of RMSE and max error. The effect of traffic noise reduction was the greatest value of the types of analysis in this research. CONCLUSIONS : From the results of the error analysis, the application method for categorizing vehicle types related to the 12-vehicle classification based on previous research is appropriate to the ANC system. However, the performance of a predictive model on an ANC system is up to a value of traffic noise reduction. By the same token, the most appropriate method that influences the maximum reduction effect is found in the third method of traffic analysis. This method has a value of traffic noise reduction of 31.28 dB(A). In conclusion, research for detecting the friction noise between a tire and the road surface for the 12 vehicle types needs to be conducted to authentically demonstrate an ANC system in the Republic of Korea.