• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.616-619
• /
• 1996

This paper presents the configuration methodology of Distributed Control System(DCS)s for process plant and their performance evaluation. Performance evaluation is specified both in terms of operational and installation aspects of system. In order to evaluate performance criteria of operational aspect, a simulation method is proposed. Modeling of system components including process computer, database, process controllers and LANs, etc, is implemented for simulation. Based on these characteristics, different system configurations are evaluated and compared through results about evaluation criteria in order to select the best DCS for particular process. The results, in abbreviated form, of the performance evaluation of DCS controlling a CAL(Continuous Annealing Line) plant of iron process are presented.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.11
• /
• pp.1676-1681
• /
• 2012

In network-based control system, measurement and control information is delivered by the network messages. Therefore, the performance of control system is directly affected by the performance of network system. System engineers need to evaluate the performance of network system to maintain the entire system in stable condition. BACnet is an international standard data communication protocol especially designed for building automation and control systems. In this study, experimental performance evaluation methodologies of BACnet MS/TP protocol and their practical evaluation results are presented.

• Proceedings of the KSME Conference
• /
• 2007.05a
• /
• pp.915-920
• /
• 2007

A new optimal state feedback and disturbance feedforward control design in the sense of minimizing $L_{2}-gain$ from disturbance to control output is proposed for disturbance attenuation of systems with bounded control input and measurable disturbance. The controller is derived in the framework of linear matrix inequality(LMI) optimization. A gain scheduled state feedback and disturbance feedforward control design is also suggested to improve disturbance attenuation performance. The control gains are scheduled according to the proximity to the origin of the state of the plant and the magnitude of disturbance. This procedure yields a stable linear time varying control structure that allows higher gain and hence higher performance controller as the state and the disturbance move closer to the origin. The main results give sufficient conditions for the satisfaction of a parameter-dependent performance measure, without violating the bounded control input condition.

• Journal of the Korean Society for Precision Engineering
• /
• v.24 no.11
• /
• pp.59-65
• /
• 2007

A new optimal state feedback and disturbance feedforward control design in the sense of minimizing $L_2$-gain from disturbance to control output is proposed for disturbance attenuation of systems with bounded control input and measurable disturbance. The controller is derived in the framework of linear matrix inequality(LMI) optimization. A gain scheduled state feedback and disturbance feedforward control design is also suggested to improve disturbance attenuation performance. The control gains are scheduled according to the proximity to the origin of the state of the plant and the magnitude of disturbance. This procedure yields a stable linear time varying control structure that allows higher gain and hence higher performance controller as the state and the disturbance move closer to the origin. The main results give sufficient conditions for the satisfaction of a parameter-dependent performance measure, without violating the bounded control input condition.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.20 no.2
• /
• pp.399-407
• /
• 1996

A fuzzy learning algorithm to get the optimal fuzzy rules is presented in this paper. The algorithm introduces a reference model to generate a desired output and a performance index funtion instead of the performance index table. The performance index funtion is a cost function based on the error and error-rate between the reference and plant output. The cost function is minimized by a gradient method and the control input is also updated. In this case, the control rules which generate the desired response can be obtained by changing the portion of the error-rate in the cost funtion. In SISO(Single-Input Single- Output)plant, only by the learning delay, it is possible to experss the plant model and to get the desired control rules. In the long run, this algorithm gives us the good control rules with a minimal amount of prior informaiton about the environment.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.318-322
• /
• 1996

In this study, a controller design method is proposed for controlling the discrete-time chaotic systems efficiently. Our proposed control method is based on Generalized Predictive Control and uses NARMAX models as a controlled model. In order to evaluate the performance of our proposed controller design method, a proposed controller is applied to Henon system which is a discrete-time chaotic system, and then the control performance of the proposed controller are compared with those of the previous model-based controllers through computer simulations. Through simulations, it is shown that the control performance of the proposed controller is superior to that of the conventional model-based controller.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.5
• /
• pp.414-421
• /
• 2007

This paper presents a engine/brake integrated VDC(Vehicle Dynamic Control) system using neural network algorithm methods for wheel slip and yaw rate control. For stable performance of vehicle, not only is the lateral motion control(wheel slip control) important but the yaw motion control of the vehicle is crucial. The proposed NNPI(Neural Network Proportional-Integral) controller operates at throttle angle to improve the performance of wheel slip. Also, the suggested NNPID controller performs at brake system to improve steering performance. The proposed controller consists of multi-hidden layer neural network structure and PID control strategy for self-learning of gain scheduling. Computer Simulation have been performed to verify the proposed neural network based control scheme of 17 dof vehicle dynamic model which is implemented in MATLAB Simulink.

• Journal of international Conference on Electrical Machines and Systems
• /
• v.1 no.2
• /
• pp.53-57
• /
• 2012

The mathematical model of a three-phase voltage source pulse-width modulation (PWM) DC/AC inverter is non-linear, and in view of the traditional linear control strategy it can not meet the requirements of designing a high-performance inverter. What's more, when the loads are not pure resistive loads, the inverter further requires that the controller possess high-performance. This paper proposes a nonlinear control strategy for the inverter called Passivity-based Control. We can alter the inverter model in three-phase abc coordinate to two-phase synchronous rotating dq coordinate for establishing the port-control Hamiltonian (PCH) model for this system. We can control the distribution of energy in the system to achieve the control aim. Simulation results show that the passivity-based control method can make this system possess a level of high-performance that is both robust and dynamic.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.6
• /
• pp.375-382
• /
• 2016

This paper realizes control of the motion of a swimming robot fish in order to implement an underwater robot fish aquarium. And it implements positional control of a two-axis trajectory path of the robot fish in the aquarium. The performance of the robot was verified though certified field tests. It provided excellent performance in driving force, durability, and water resistance in experimental results. It can control robot motion, that is, it recognizes an object by using an optical flow object-detecting algorithm, which uses a video camera rather than image-detecting sensors inside the robot fish. It is possible to find the robot's position and control the motion of the robot fish using a radio frequency (RF) modem controlled via personal computer. This paper proposes realization of robot fish motion-tracking control using the optical flow object-detecting algorithm. It was verified via performance tests of lead-lag action control of robot fish in the aquarium.

• Journal of the Korean Society for Precision Engineering
• /
• v.11 no.1
• /
• pp.108-113
• /
• 1994

Active noise control technique has superior performance in low frequency ranges(50 .approx. 400Hz) to the conventional passive noise control technique. For the feasibility of active noise control, it is required to develop a controller which can implement control algorithm on real-time. In this study, therefore, real-time controller is developed using TMS320c25, high speed digital processor. Unlike conventional DSP board of complete ADD ON type, it is possible for the developed controller to interface with the other computer system easily by series communication for the convenience of program development. Furthermore it is designes to be separated readily as a control device. Active noise control of duct system is implemented ti evaluate a performance of developed device. Active noise control of duct system is implemented to evaluate a performance of developed controller using filtered-x LMS algorithm.