• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.1-9
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• 2006

This paper presents the modeling and multivariable feedback control of a novel high-precision multi-dimensional positioning stage. This integrated 6-degree-of-freedom. (DOF) motion stage is levitated by three aerostatic bearings and actuated by 3 three-phase synchronous permanent-magnet planar motors (SPMPMs). It can generate all 6-DOF motions with only a single moving part. With the DQ decomposition theory, this positioning stage is modeled as a multi-input multi-output (MIMO) electromechanical system with six inputs (currents) and six outputs (displacements). To achieve high-precision positioning capability, discrete-time integrator-augmented linear-quadratic-regulator (LQR) and reduced-order linearquadratic-Gaussian (LQG) control methodologies are applied. Digital multivariable controllers are designed and implemented on the positioning system, and experimental results are also presented in this paper to demonstrate the stage's dynamic performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.14-22
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• 2001

This paper presents identification and control designs using neural networks for a class of multivariable nonlinear servomechanisms. A proposed neuro-controller is a combination of linear controllers and a neural network, and is trained by indirect neuro-control scheme. The proposed neuro-controller is implemented and tested on an IBM PC-based two 2 bar systems holding an object, and is applicable to many de-motor-driven precision multivariable nonlinear servomechanisms. The ideas, algorithm, and experimental results arc described. Moreover, experimental results are shown to be superior to those of conventional control.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.4
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• pp.250-256
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• 2001

This paper deals with a design problem of a decentralized controller with a strongly connected two-input two-output multivariable system. To this end, we present a classical design approach which consists of two main steps: one is to decompose the multivariable plant into two single-input single-output systems by means of the Individual Channel Design (ICD) concept, the other is to design controller of each channel by the Coefficient Diagram Method (CDM) so that it satisfies, especially, time domain specifications such as settling time, overshoot etc.. A design procedure was proposed and then was applied to a 2$\times$2 hot rolling mill plant. Simulation results showed that the proposed method has excellent control performances.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.808-813
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• 1999

A fuzzy learning method to control an unstable and multivariable system is presented in this paper, Because the multivariable system has generally a coupling effect between the inputs and outputs, it is difficult to find its modeling equation or parameters. If the system is unstable, initial condition rules are needed to make it stable because learning is nearly impossible. Therefore, this learning method uses the initial rules and introduces a cost function composed of the actual error and error-rate of each output without the modeling equation. To minimize the cost function, we experimentally got the Jacobian matrix in the operating point of the system. From the Jacobian matrix, we can find the direction of the convergence in the learning, and the optimal control rules are finally acquired when the fuzzy rules are updated by changing the portion of the errors and error rates.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.841-846
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• 1990

In this study, a design method to obtain a robust suboptimal regulator for linear multivariable system is presented. This new design method is based on the optimal regulator design method using eigen-structure assignment and it uses additional cost function which represent robustness of the closed loop system. When we design the regulator using pole assignment method for linear multivariable system we have extra degree-of-freedom after assigning desired eigenvalues of the closed loop system in determining the feedback gain. So we assign additional robust suboptimal regulator. In this study we also feedback the system output for more practical applications.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.32-37
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• 1998

This paper deals with the implementation of a robust multivariable controller using DSP board and the application to real systems. The LQG/LTR (Linear Quadratic Gaussian with Loop Transfer Recovery) controller proposed by Doyle et al.[1,2] is adopted to design the control system. A helicopter propeller setup is taken as the controlled system in the current paper, and the mathematical model is derived to design the multivariable controller. The performance of the controller is evaluated via simulations, and implementation and application to the MIMO system shows that the control performances are satisfactory and superior to those of the PID controller.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1086-1097
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• 1990

The design of multivariable control systems using the Inverse Nyquist Array method is described in this paper. The INA is a simple design technique, which permits a designer to achieve his objectives for a controller specification in a step-by-step fashion using Gershgorin band and Ostrowski circle. The application to a multivariable system of CANDU nuclear power plant with 5 inputs, 8 outputs, and 24 state variables is reviewed and the simulation shows satisfactory results.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.136-144
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• 1999

This paper deals with the fault detection problem in uncertain linear multivariable systems and its application. A robust fault detection method presented by Kim et a. (1998) for MIMO (Multi Input/Multi Output) systems has been adopted and applied to the twin rotor MIMO experimental setup using industrial DSP. The system identification problem is formulated for the twin rotor MIMO system and its parameters are estimated using experimental data. Based on the estimated parameters, some fault detection simulations are performed using the robust fault detection method, which shows that the preformance is satisfied.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.154-166
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• 1992

A kinematic nonlinear multivariable launcher is modeled of which the azimuth and elevation axes are drived simultaneously and position control systems are designed for this system by the PD and LQG/LTR control methods. Also, the suitable command input fonction is suggested for the desired command following performance and the two control systems with disturbances and load variation are evaluated for the entire operating range by computer simulation. It is found that the two linear controllers can be used for the kinematic nonlinear multivariable launcher in the entire operating range and LQG/LTR controller is more effective for disturbance rejection.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.78-86
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• 2003

Multivariable control system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, Pill Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the Pill controller has to be manually tuned by trial and error. This paper suggests a tuning method of the Pill Controller for the multivariable power plant using an immune algorithm, through computer simulation. Tuning results by immune algorithms based neural network are compared with the results of genetic algorithm.