• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.281-287
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• 2001

In this paper, we deal with a decentralized control scheme with input compensation form for gantry crane systems. By considering the gantry cranes characteristics, the system is decentralized into two subsystems, the travelling and swaying subsystem and the hoisting subsystem. For decentralizing the system, a simple algorithm is proposed using the observability canonical form. The decentralized subsystems include unknown inputs that one coupled with other subsystems and actuator failures. These unknown input and actuator failures are estimated by using PI observation techniques. And those estimated values are used to construct an input compensation form. Finally, the proposed decentralized control scheme for the gantry crane systems is verified by crane simulation.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.927-929
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• 1996

In this paper, we considers the problem of designing an observer for bilinear systems with unknown input. A sufficient condition for the asymptotic stability of the proposed observer is derived by means of delectability, invariant zeros, and stable subspace. In sufficient condition, the bound which guarantees the asymptotic stability was derived, which based on the Lyapunov stability. And Observer existing conditions are suggested in various cases. Through a simple example, we derived the observer structure and the bound which guarantees the asymptotic stability.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.734-738
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• 1996

An adaptive output feedback controller is designed for tracking control of an n-link robot manipulator with unknown load. High-gain observers with same structure as error dynamic systems are used to estimate joint velocities. The parameter adaptation is achieved by the smoothed projection algorithm. The control inputs are saturated outside a domain of interest. Simulation results on a 2-link manipulator illustrate that when the speed of the high-gain observer is sufficiently high, the proposed controller recovers the performance under state feedback control.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1169-1172
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• 1996

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilization of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the known and unknown systems with constrained inputs and/or states. The nature of neural networks as a parallel processor would circumvent the problem of "curse of dimensionality". The control law has been demonstrated for a velocity input type motor identified by a genetic algorithm called GENOCOP.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.372-377
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• 1996

A time-optimal control law for quick, strongly nonlinear systems like revolute robots has been developed and demonstrated. This procedure involves the utilization of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the known and unknown systems with constrained inputs and/or states. The nature of neural networks as a parallel processor would circumvent the problem of "curse of dimensionality".ity".uot;.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.70-74
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• 1989

To track the target trajectory with maneuvers, unknown maneuvering inputs must be estimated. To do this the direct estimation algorithm using generalized least square technique is developed based on the procedure of failure detection and identification(FDI) theory. Through the simulation using maneuvering target scenario, tracking performance and efficiency of the algorithm developed here are investigated.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.89.3-89
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• 2002

Plasma processing plays a crucial role in fabricating integrated circuits (ICs). Manufacturing ICs in a cost effective way, it is increasingly demanded a computer model that predicts plasma properties to unknown process inputs. Physical models are limited in the prediction accuracy since they are subject to many assumptions. Expensive computation time is another hindrance that prevents their widespread used in manufacturing site. To circumvent these difficulties inherent in physical models, neural networks have been used to learn nonlinear plasma data. A generalized regression neural network (GRNN) [I] is one of the architectures that have been widely used to analyze complex chemical data. I...

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.46.2-46
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• 2002

Plasma processing plays a crucial role in fabricating integrated circuits (ICs). Manufacturing ICs in a cost effective way, it is increasingly demanded a computer model that predicts plasma properties to unknown process inputs. Physical models are limited in the prediction accuracy since they are subject to many assumptions. Expensive computation time is another hindrance that prevents their widespread used in manufacturing site. To circumvent these difficulties inherent in physical models, neural networks have been used to learn nonlinear plasma data [1]. Among many types of networks, a backpropagation neural network (BPNN) is the most widely used architecture. Many training variables are...

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.101-106
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• 1990

The problem of constructing an observer for use in the control of superheater temperature with desuperheater is considered. The distributed heat input into the superheater is usually not available for use in the observer, and hence is treated as a disturbance. The observer theory for systems with unknown inputs is exploited and applied to the problem. Approximation of the heat input utilizing the specific heat input distribution pattern is also considered.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.513-523
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• 2012

For precisely regulating the speed of a permanent magnet synchronous motor system with unknown load torque disturbance and disturbance inputs, an LMI-based sliding mode control scheme is proposed in this paper. After a brief review of the PMSM mathematical model, the sliding mode control law is designed in terms of linear matrix inequalities (LMIs). By adding an extended observer which estimates the unknown load torque, the proposed speed tracking controller can guarantee a good control performance. The stability of the proposed control system is proven through the reachability condition and an approximate method to implement the chattering reduction is also presented. The proposed control algorithm is implemented by using a digital signal processor (DSP) TMS320F28335. The simulation and experimental results verify that the proposed methodology achieves a more robust performance and a faster dynamic response than the conventional linear PI control method in the presence of PMSM parameter uncertainties and unknown external noises.