• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.325-328
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• 1997

In this paper the modeling and control of a twin roll strip caster is investigated. Mathematical models for the strip casting process are obtained by analyzing five critical areas such that the molten steel level in the pool, solidification process, roll separating force and torque, roll dynamics including hydraulic actuators, and roll drive system. A two-level control strategy is proposed. At lower level, three local subsystems are independently feedback-controlled by suitable local controllers which perform well to the behaviors of each subsystem. They are a variable structure control of the molten steel level in the pool, an adaptive predictive control of the roll gap which is directly related to the strip thickness, and an $H^{\infty}$ control of the roll drive system. At higher level, all reference signals to the lower level subsystems are generated by an optimal controller in the perspective of regulating the strip thickness and roll separating force. Simulations are provided..

• Proceedings of the KIPE Conference
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• 1996.06a
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• pp.17-20
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• 1996

An adaptive current control scheme of a permanent magnet (PM) synchronous motor with the simultaneous estimation of the magnitude of the flux linkage and stator resistance is proposed. The adaptive parameter estimation is achieved by a model reference adaptive system (MRAS) technique. The adaptive laws are derived by the Popov's hyperstability theory and the positivity concept. The predictive control scheme is employed for the current controller with the estimated parameters. The robustness of the proposed current control scheme is compared with the conventional one through the computer simulations.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.539-541
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• 1994

The average current mode control of zero current switched series resonant converter is proposed. The conventional current controllers such as bang bang current controller and predictive current cantroller have some demerits like current offset and complexity. In this paper, the proposed current control technique are conventional current control techniques are comparatively studied. By the proposed control technique. the current cantroller can be simplely implemented without current offset.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.3
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• pp.136-141
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• 2007

In this paper, a new simple control method for active power filter which can realized the complete compensation of the harmonic currents is proposed. In the proposed scheme, a compensating current reference generator employing resonance model implemented by a DSP(Digital Signal Processor) is introduced. Deadbeat control is employed to control the active power filter. The switching pulse width based SVM(Space Vector Modulation) is adopted so that the current of active power filter is been exactly equal to its reference at the next sampling instant. To compensate the computation delay of digital controller, the prediction of current is achieved by the current observer with deadbeat response.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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• pp.300-314
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• 2017

This paper examines finite rotation angle (FRA) applications for spacecraft attitude control. The coordinate transformation matrix and the attitude kinematics represented by FRAs are introduced. The interpolation techniques for the angular orientations are thoroughly investigated using the FRAs and the results are compared to those using traditional methods. The paper proposes trajectory description techniques by using extremely smooth polynomial functions of time, which can describe point-to-point attitude maneuvers in a realizable and accurate manner with the help of unique FRA features. In addition, new controller design techniques using the FRAs are developed by combining the proposed interpolation techniques with a model predictive control framework. The proposed techniques are validated through their attitude control applications for an aggressive point-to-point maneuver. Conclusively, the FRAs provide much more flexibility than quaternions and Euler angles when describing kinematics, generating trajectories, and designing attitude controllers for spacecraft.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.327-330
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• 1996

It is well known that the controller output limits have a signifiant effect on the closed loop system performance. Considering the input constraints in GPCF, an effective selection method of the control weighting(.gamma.) is proposed to reduce the amplitude and the rate of control signals so that control signals lie within the limits. It is based on the relation between control weighting(.gamma.) and optimal solution of the unconstrained GPCF. The GPCFIC algorithm chooses an .gamma. at each sampling time so that all unconstrained GPCF output over the control horizon satisfy the rate and the amplitude constraints. In order to evaluate the performance of the GPCFIC, the computer simulations have been done for level control of PWR steam generator in low power operation and shown satisfactory results.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.159-163
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• 2001

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. for constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an offset error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.765-770
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• 2005

This paper presents a new intelligent digital redesign (IDR) method via the compensated bilinear transformation to design the digital controller such that the digital fuzzy system is equivalent to the analog fuzzy system in the sense of the state-matching. This paper especially consider a multirate control scheme with a predictive feature, where the digital control input is held constant N times between the sampling points. More precisely, the multirate control scheme is proposed that utilizes a numerical integration scheme to approximately predict the current state from the state measured at the sampling points, the delayed measurements. For this system, the IDR conditions incorporated with stabilizability in the format of the linear matrix inequalities (LMIs) are derived. The superiority of the proposed technique is convincingly visualized through a numerical example.