• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.786-791
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• 2005

This paper is concerned with the examination and evaluation concerning a tuning method of multivariable PID controllers based on partial model matching on frequency domain proposed by authors from practical view point. In this case, PID controller parameters are determined by minimizing the loss function defined by the difference between frequency response of ideal model transfer function and actual frequency response on several frequency points. The purpose of the paper is to examine and evaluate the performance of the method through actual experiments of MIMO liquid level experimental process control equipment.

• Transactions on Control, Automation and Systems Engineering
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• 제3권4호
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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.

• 제어로봇시스템학회지
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• 제1권1호
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• pp.16-16
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• 1995

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• Transactions on Control, Automation and Systems Engineering
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• 제1권1호
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• pp.16-20
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• 1999

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.736-741
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• 1992

The Stewart platform is one example of a motion simulator which generates 6 DOF motion in space by 6 actuators connected in parallel. The present SISO controllers are designed to track displacement command of each actuator computed from reference 6 DOF motion of platform by Stewart platform inverse kinematics. But this type of control can't cope with external load variation, geometric configuration of motion simulator, and different dynamic behavior of 6 DOF motion. In this paper, a multivariable controller using H- optimal control theory is designed for linerized simulator model with each actuator driving force as control input and platform 6 DOF motion as measured output. Nonlinear simulation result of the H$_{\infty}$ MIMO controller is not satisfied in steady-state characteristics. But the proposed H$_{\infty}$ + PI control scheme shows acceptable performance.e.e.

• International Journal of Control, Automation, and Systems
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• 제5권2호
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• pp.212-217
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• 2007

A new method of designing multi-loop PID controllers is presented in this paper. By using the generalized IMC-PID method for multi-loop systems, the optimization problem involved in finding the PID parameters is efficiently simplified to find the optimum closed-loop time constant in a reduced search space. A weighted sum Mp criterion is proposed as a performance cost function to cope with both the performance and robustness of a multi-loop control system. Several illustrative examples are included to demonstrate the improved performance of the multi-loop PID controllers obtained by the proposed design method.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.311-316
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• 1994

In this study, a governing equation for 4-axis active magnetic bearing system composed of a rigid rotor and two radial magnetic bearings is derived. We find out that there are two kind of coupling between control axes in the system. And digital contralized controller is designed based on state-space approach and linear quadratic regulator(LQR) theory. By numerical simulation, it is shown what the designed controller can stabilize the system and control the coupling effectively using limited control input.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.407-409
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• 1998

In order to improve the tracking performance of $2{\times}2$ multivariable control systems, a fuzzy control algorithm with feedforward compensator is represented. The method consists in two steps. First, neglecting interconnections. one designs a fuzzy controller to each individual loop. In the second stage, low-order transfer functions of outputs to reference inputs are estimated. We propose a design method of the feed forward compensator based on the transfer functions. An illustrative example are shown.

• 한국식품저장유통학회지
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• 제9권2호
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• pp.137-143
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• 2002

Temperature and humidity are the most important factors and should be effectively controlled for the cold storage of graius. Fuzzy logic can be easily implemented to the MIMO(Multi-Input Multi-Output) control systems. For the cold storage in grain bin, fuzzy logic was applied to an air conditioning system. The capacities of the grain bin and the air conditioner are 80 tons and 30㎾, respectively. Also, the target values of temperature and relative humidity in outlet duct of the air conditioner were 8$\^{C}$ and 75%, respectively. In order to control temperature and relative humidity of air, a damper in inlet duct was manipulated for temperature control and a heater was used for humidity control. Temperature deviation and change of temperature deviation were used as input parameters for the fuzzy system. Humidity was only considered as a load. The experimental results showed that the controlled temperature of exhausted air was maintained at 8$\pm$2$\^{C}$. Relative humidity of the air was also controlled at the target relative humidity of 50∼80%.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2283-2285
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• 2001

This paper proposed to a neural network based fuzzy control (neuro-fuzzy control) technique for attitude control of helicopter with strongly dynamic nonlinearities and derived a helicopter aerodynamic torque equation of helicopter and the force balance equation. A neuro-fuzzy system is a feedforward network that employs a back-propagation algorithm for learning purpose. A neuro-fuzzy system is used to identify nonlinear dynamic systems. Hence, this paper presents methods for the design of a neural network(NN) based fuzzy controller(that is, neuro-fuzzy control) for a helicopter of nonlinear MIMO systems. The proposed neuro-fuzzy control determined to a input-output membership function in fuzzy control and neural networks constructed to improve through learning of input-output membership functions determined in fuzzy control.