• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.722-728
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• 2003

In this paper, we propose a fuzzy traffic controller of Sugeno`s fuzzy model so as to model the nonlinear characteristics of controlling the traffic light. It use a degree of the traffic congestion of the preceding roads as an input so that it can cope with traffic congestion appropriately, which causes the loss of fuel and our discomfort. First, in order to construct fuzzy traffic controller of Sugeno`s fuzzy model, we model the control process of the traffic light by using Mamdani`s fuzzy model, which has the uniform membership functions of the same size and shape. Second, we make Mamdani`s fuzzy model with the non-uniform membership functions so that it can exactly reflect the knowledge of experts and operators. Last, we construct the fuzzy traffic controller of Sugeno`s fuzzy model by learning from the input/output data, which is retrieved from Mamdani`s fuzzy model with the non-uniform membership functions. We compared and analyzed the fixed traffic light controller, the fuzzy traffic controller of Mamdani`s fuzzy model and the fuzzy traffic controller of Sugeno`s fuzzy model by using the delay time and the proportion of the entered vehicles to the occurred vehicles. As a result of comparison, the fuzzy traffic controller of Sugeno`s fuzzy model showed the best performance.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.481-487
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• 2013

This paper presents an FIR (Finite Impulse Response) fixed-interval smoothing filter for fast and exact estimating state variables of a discrete nonlinear system with modeling uncertainty. Conventional IIR (Infinite Impulse Response) filter and smoothing filter can estimate state variables of a system with an exact model when the system is observable. When there is an uncertainty in the system model, however, conventional IIR filter and smoothing filter may cause large errors because the filters cannot estimate the state variables corresponding to the uncertain model exactly. To solve this problem, FIR filters that have fast estimation properties and have robustness to the modeling uncertainty have been developed. However, there is time-delay estimation phenomenon in the FIR filter. The FIR smoothing filter proposed in this paper makes up for the drawbacks of the IIR filter, IIR smoothing filter, and FIR filter. Therefore, the FIR smoothing filter has good estimation performance irrespective of modeling uncertainty. The proposed FIR smoothing filter is applied to the integrated navigation system composed of a magnetic compass based DR (Dead Reckoning) and a GPS (Global Positioning System) receiver. Even when the magnetic compass error that changes largely as the surrounding magnetic field is modeled as a random constant, it is shown that the FIR smoothing filter can estimate the varying magnetic compass error fast and exactly with simulation results.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.618-628
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• 2016

Active power filter (APF) has been proved as a flexible solution for compensating the harmonic distortion caused by nonlinear loads in power distribution power systems. Digital repetitive control can achieve zero steady-state error tracking of any periodic signal while the sampling points within one repetitive cycle must be a known integer. However, the compensation performance of the APF would be degradation when the grid frequency varies. In this paper, an improved repetitive control scheme with frequency adaptive capability is presented to track any periodic signal with variable grid frequency, where the variable delay items caused by time-varying grid frequency are approximated with Pade approximants. Additionally, the stability criterion of proposed repetitive control scheme is given. A three-phase shunt APF experimental platform with proposed repetitive control scheme is built in our laboratory. Simulation and experimental results demonstrate the effectiveness of the proposed repetitive control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.474-479
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• 1998

In this paper, we present a mathematical model of the piezoelectric pump and its application to the automobile brake system. The piezoelectric pump consists of a multi-layered piezoelectric element a diaphragm, pumping values, resonant pipes and accumulators, and the maximum pumping power of 62W nab obtained in the previous experiments by using the piezoelectric element of 22mm diameter and 55.5mm length. A detailed mathematical model of the pump is derived here by considering the compressibility of the working oil, nonlinear characteristics of piezoelectric element, the time delay of pumping values' action and be on. The validity of the model is illustrated by comparing the experimental data and the simulation results. Using the mathematical model of the piezoelectric pump, a brake system for automobile disk brake is also simulated in this paper. The brake system consists of a piezoelectric pump as a power source, calipers and its piston to generate brake force, and a three position solenoid value to change the brake situation. It is shown that 15mm/sec of piston speed and 20kN of piston force are obtained by using the piezoelectric element of 33mm diameter and 55.5mm length.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.152-161
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• 2017

The CBTC(Communication based Train Control) system employed on 2.4GHz ISM band adopt the IEEE802.11.x standard. Therefor communication time delay, distortion and data losses will be produced. To overcome these problems, CPSK(Continuous Phase shift keying) modulated DS/SS(Direct Sequence Spread Spectrum) transmitter with 908MHz carrier frequency is proposed. Through the eye diagram and scatter diagram the performance is analysed. And the phase noise characteristic of the local oscillator is measured about 60dBc/Hz, this means that the phase noise performance is very good compared to the signal. Reference frequency suppression of the 2nd local oscillator is 50dB below compared to the signal. Through the polarities of the complementary signal equally probable, the line spectrum of the output spectrum is eliminated. The nonlinear effects which introduce the spectral spreading or spectral regeneration is reduced remarkably.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.395-400
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• 2017

The paper discussed the air-to-fuel ratio control of automotive fuel-injection systems using the cerebellar model articulation controller(CMAC) neural network. Because of the internal combustion engines and fuel-injection's dynamics is extremely nonlinear, it leads to the discontinuous of the fuel-injection and the traditional method of control based on table look up has the question of control accuracy low. The advantages about CMAC neural network are distributed storage information, parallel processing information, self-organizing and self-educated function. The unique structure of CMAC neural network and the processing method lets it have extensive application. In addition, by analyzing the output characteristics of oxygen sensor, calculating the rate of fuel-injection to maintain the air-to-fuel ratio. The CMAC may easily compensate for time delay. Experimental results proved that the way is more good than traditional for petrol control and the CMAC fuel-injection controller can keep ideal mixing ratio (A/F) for engine at any working conditions. The performance of power and economy is evidently improved.