• Journal of IKEEE
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• v.3 no.2 s.5
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• pp.250-258
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• 1999

This paper presents a decentralized adaptive controller design for a Magnetically Levitated Fine Manipulator to follow the given trajectory as close as possible in spite of coupling effects between motion axes(degree of freedoms or subsystems). The present controller consists of two parts: the model reference controls based on known subsystems and the local adaptive controls. The former stabilizes the motion of the manipulator so as to follow that of the reference model. The latter reduces tracking errors due to coupling disturbances by adjusting the local gains to such levels that override interactions and assure the stability of the overall system. Through several experimental results, it has been shown that the decentralized adaptive control scheme has better tracking performances comparing to the PID controller case as well as good disturbance(coupling) rejection property.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.33 no.4
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• pp.290-297
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• 1997

Underwater robotic vehicles(URVs) are used for various work assignments such as pipe-lining, inspection, data collection, drill support, hydrography mapping, construction, maintenance and repairing of undersea equipment, etc. As the use of such vehicles increases the development of vehicles having greater autonomy becomes highly desirable. The vehicle control system is one of the most critic vehicle subsystems to increase autonomy of the vehicle. The vehicle dynamics is nonlinear and time-varying. Hydrodynamic coefficients are often difficult to accurately estimate. It was also observed by experiments that the effect of electrically powered thruster dynamics on the vehicle become significant at low speed or stationkeeping. The conventional linear controller with fixed gains based on the simplified vehicle dynamics, such as PID, may not be able to handle these properties and result in poor performance. Therefore, it is desirable to have a control system with the capability of learning and adapting to the changes in the vehicle dynamics and operating parameters and providing desired performance. This paper presents an adaptive and learning control system which estimates a new set of parameters defined as combinations of unknown bounded constants of system parameter matrices, rather than system parameters. The control system is described with the proof of stability and the effect of unmodeled thruster dynamics on a single thruster vehicle system is also investigated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.691-695
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• 1998

Most of systems has nonlinearity . And also accurate modelings of these uncertain nonlinear systems are very difficult. In this paper, a fuzzy modeling technique for the stabilization control of an IP(inverted pendulum) system with nonlinearity was proposed. The fuzzy modeling was acquired on the basis of ANFIS(Adaptive Neuro Fuzzy Infernce System) which could learn using a series of input-output data pairs. Simulation results showed its superiority to the PID controller. We believe that its applicability can be extended to the other nonlinear systems.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.57-64
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• 2015

In this paper, a model reference adaptive control (MRAC) scheme is applied for the precise and robust motion control of a pneumatic system with load variation. The reference model for MRAC is designed systematically using linear quadratic Gaussian control with loop transfer recovery (LQG/LTR). The sigmoid function of inverse velocity is used to compensate for the nonlinear friction force between the sliding parts. The experimental results show that MRAC effectively overcame the limit of the PID controller when there was unknown disturbance, including abrupt load variation and model uncertainty in the pneumatic control system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.496-499
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• 1997

Field robot represented by excavator can be applied for various kinds of working in manufacturing, construction, agriculture etc. because of the flexibility of its multi-joint mechanism and the high power of hydraulic actuators. In general, the dynamics of field robot have strong coupling, various kinds of non-linearity, and time-varying parameters according to working conditions. Therefore, it is very difficult to describe the system well, and design controller systematically based on its model. This paper established the mathematical model of field robot driven by electro-hydraulic servomechanism and constructed the adaptive control system robust to external load variations. The proposed control system for the field robot was evaluated by the computer simulation and the performance results of trajectory tracking were compared with that of PID control system.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.179-182
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• 2002

The position information of the rotor are required while the SRG(Switched Reluctance Generator) is drived. The position information is generally provided by shaft encoder or resolver. But it is weak in the dusty, high temperator and EMI environment. Therefore, the sensor is able to required to eliminated from the SRG. In this paper, a estimation algorithm for the rotor position of the SRG is introducted and a constant DC-link voltage is controled by PID controller. The estimation algorithm is implemened by the adaptive sliding observer and that it is able to estimate the rotor position well is proved by the simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.60-66
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• 1996

Model Based Predictive Control(MBPC) has been widely used in predictive control since 80's. GPC[1] which is the superset of many MBPC strategies a popular method, but GPC has some weakness, such as insufficient stability analysis, non-applicability to internally unstable systems. However, CRHPC[2] proposed in 1991 overcomes the above limitations. So we chose RHPC based on CRHPC for electric furnace control. An electric furnace which has nonlinear properties and large time delay is difficult to control by linear controller because it needs nearly perfect modelling and optimal gain in case of PID. As a result, those controls are very time-consuming. In this paper, we applied RHPC with equality constraint to electric furnace. The reults of experiments also include the case of RHPC with monotonic weighting improving the transient response and including unmodelled dynamics. So, This paper proved the practical aspect of RHPC for real processes.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.146-151
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• 1992

A two-compartment four-cell model is developed for the adiabatic autoclave slim type reactor for free radical polymerization of low density polyethylene(LDPE). The mass and energy balances give rise to a set of ordinary differential equations, and by analyzing the system it is possible to predict properly not only the reactor performance but also the properties of polymer product. The steady state multiplicity is found to exist and examined by constructing the bifurcation diagram. The effects of various operation parameters on the reactor performance and polymer properties are investigated systematically to show that the temperature distribution plays the central role for the properties of polymer product. Therefore, it is essential to establish a good control strategy for the temperature in each compartment. In this study it is shown that the reactor system can be adoptively controlled by pole-placement algorithm with conventional PID controller. To accomplish a satisfactory control, the estimator and controller are initialized during the period of start-up.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.3
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• pp.215-224
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• 2008

In this paper, we introduced a linear motor-based transfer system with an active pid controller which can be replaced with an automated guided vehicle (AGV) for the port automation. This system, which is named LMCTS(liner motor-based container transfer system), is based on PMLSM (permanent magnetic linear synchronous motor) which basically consists of stator modules on the rail and shuttle car. Therefore more progressive and adaptive control mechanisms should be required to control a system with large variation of container weight, the difference of each characteristic of stator modules, a stator module's trouble etc. We introduced an active control mechanism with an online tuning scheme using modified evolutionary strategy. Some computer simulations are implemented to assess the robustness of the proposed system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.405-413
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• 2006

In general mechanical servo systems, friction deteriorates the performance of controllers by its nonlinear characteristics. Especially, friction phenomenon causes steady-state tracking errors and limit cycles in position and velocity control systems, even though gains of controllers are tuned well in linear system model. Even if sensor is used higher accuracy level, it is difficult to improve tracking performance of the position to the same level with a general control method such as PID type. Therefore, many friction models were proposed and compensation methods have been researched actively. In this paper, we consider that the variation of mover's mass is various by loading and unloading. The normal force variation occurs by it an other parameters. Therefore, the proposed control system is composed of main position controller and a friction compensator. A parameter estimator for a nonlinear friction model is designed by adaptive control law and adaptive backstepping control method.