• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.194-211
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• 1993

This paper proposes an optimal incentive scheme for environmental pollution and output control in oligopoly markets under asymmetric information situation where the regulator has no information about each firm's technology on output productions and pollution abatements. We compare two interesting optimal incentive schemes (one is static model previously proposed and the other is dynamic model suggested in this paper), analyze features of these schemes, and carefully discuss its relevances to other schemes.

• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1557-1566
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• 2006

We present a procedure for the application of global orthogonal polynomial into an atmospheric re-entry maneuvering problem. This trajectory optimization is imbedded in a family of canonically parameterized optimal control problem. The optimal control problem is transcribed to nonlinear programming via global orthogonal polynomial and is solved a sparse nonlinear optimization algorithm. We analyze the optimal trajectories with respect to the performance of re-entry maneuver.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.5
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• pp.630-636
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• 1999

In this paper in optimal modulation controller for position control and anti-sway of container crane systems is designed by a recursive algorithm that determines the state weighting matrix Q of a linear quadratic performance. The optimal modulation controller is based on optimal control. The basic feature of the recursive algorithm is the reduction of the number of iterations as well as minimization of the calculations involved So in order to obtain a mathematical model which rep-resents the equation of motion of the trolley and load Lagrange equation is used. The optimal modulation controller has been verified and simulated to show that it is robust when a load dis-turbance is applied and a reference is changed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.688-697
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• 2017

It is very difficult to solve mathematically the optimal control problem for non - linear mobile robots to move to target points with minimum energy related to velocity, acceleration and angular velocity in minimum time. This paper proposes a method to obtain optimal control gains with which mobile robots move with minimum energy related to velocity, acceleration and angular velocity in minimum time using genetic algorithms. Mobile robots are non - linear systems so that their optimal control gains depend on initial positions. Hence initial positions are divided into some partition points and optimal control gains are obtained at each partition point with genetical algorithms. These optimal control gains are used to train neural networks that generate proper control gains at arbitrary initial position. Finally computer simulation studies have been conducted to verify the effectiveness of the method proposed in this paper.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2502-2512
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• 2000

Truss structures are widely used in many space structures, such as large antenna systems, space stations, precision segmented telescopes because they are light in weight and amenable in assembly or deployment. But, due to the low damping capacity, they remain excited for a long time once disturbed. These structural vibrations can reduce life of the structures and cause unstable dynamic characteristics. In this research, vibration suppression experiment has carried out with a three-dimensional 15-member truss structure using two piezoelectric actuators. Piezoelectric actuators which consist of stacks of thin piezoelectric material disks are directly inserted to the truss structure collocated with the strain sensors. Each actuator is controlled digitally in decentralized manner, based on local integral and proportional feedback. The optimal positions of the actuators are determined by the modal damping ratio and the control force. Numerical simulation has carried out to determine optimal position of each actuator.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.11
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• pp.1068-1076
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• 2004

Technology on the proportional integral (PI) control have grown rapidly owing to the needs for the robust capacity of the controllers from industrial building sectors. However, PI controller requires tuning of gains for optimal control when the outside weather condition changes. The present study presents the possibility of reinforcement learning (RL) control algorithm with PI controller adapted in the HVAC system. The optimal design criteria of RL controller was proposed in the Environment Chamber experiment and a theoretical analysis was also conducted using TRNSYS program.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.460-467
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• 2018

Metaheuristic optimization approach has become the new framework for control synthesis. The main purposes of the control design are command (input) tracking and load (disturbance) regulating. This article proposes an optimal proportional-integral-derivative (PID) controller design for the DC motor speed control system with tracking and regulating constrained optimization by using the cuckoo search (CS), one of the most efficient population-based metaheuristic optimization techniques. The sum-squared error between the referent input and the controlled output is set as the objective function to be minimized. The rise time, the maximum overshoot, settling time and steady-state error are set as inequality constraints for tracking purpose, while the regulating time and the maximum overshoot of load regulation are set as inequality constraints for regulating purpose. Results obtained by the CS will be compared with those obtained by the conventional design method named Ziegler-Nichols (Z-N) tuning rules. From simulation results, it was found that the Z-N provides an impractical PID controller with very high gains, whereas the CS gives an optimal PID controller for DC motor speed control system satisfying the preset tracking and regulating constraints. In addition, the simulation results are confirmed by the experimental ones from the DC motor speed control system developed by analog technology.

• Journal of Korean Association for Spatial Structures
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• v.13 no.2
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• pp.37-45
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• 2013

Reduction of microvibration is regarded as important in high-technology facilities with high precision equipments. In this paper, smart control technology is used to improve the microvibration control performance. Mr damper is used to make a smart base isolation system amd fuzzy logic control algorithm is employed to appropriately control the MR damper. In order to develop optimal fuzzy control algorithm, a multi-objective genetic algorithm is used in this study. As an excitation, a train-induced ground acceleration is used for time history analysis and three-story example building structure is employed. Microvibration control performance of passive and smart base isolation systems have been investigated in this study. Numerical simulation results show that the multi-objective genetic algorithm can provide optimal fuzzy logic controllers for smart base isolation system and the smart control system can effectively reduce microvibration of a high-technology facility subjected to train-induced excitation.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1003-1007
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• 2015

Depth calibration is a procedure for finding the conversion function that maps disparity data from a depth-sensing camera to actual distance information. In this paper, we present an optimal depth calibration method for Kinect$^{TM}$ sensors based on an experimental design and convex optimization. The proposed method, which utilizes multiple measurements from only two points, suggests a simplified calibration procedure. The confidence ellipsoids obtained from a series of simulations confirm that a simpler procedure produces a more reliable calibration function.

• International Journal of Automotive Technology
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• v.8 no.6
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• pp.791-798
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• 2007

The hydraulic hybrid vehicle(HHV) is an application of hydrostatic transmission technology to improve vehicle fuel economy and emissions. A relatively lower energy density of hydraulic accumulator and complicated coordinating operations between two power sources require a special energy management strategy to maximize the fuel saving potential. This paper presents a new type of configuration for parallel HHV to minimize the disadvantages of the hydraulic accumulator, as well as a methodology for developing an energy management strategy tailored specially for PHHV. Based on an analysis of the optimal energy distribution between two power sources over a representative urban driving cycle with a Dynamic Programming(DP) algorithm, a fuzzy-based optimal torque management strategy is designed and developed to control the torque distribution. Simulation results demonstrates that the optimal torque management strategy maximizes the advantages of this hybrid type of configuration, and the high power density characteristics of hydraulic technology effectively improve the robustness of the energy management strategy and fuel economy of the PHHV.