• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.519-520
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• 2016

BLSRM is a nonlinear, strong coupling and multi-variable system. The conventional control method is vulnerable to uncertain factors such as the load disturbance and satellite parameters change. It is difficult to obtain satisfactory control effect. Basing on a 8/10 BLSRM, whose suspending force control is separated with the torque control, this paper presents adaptive fuzzy PID controller for levitation control, which apply the fuzzy logic control to the conventional PID controller for parameters self-tuning. Both fuzzy and parameters of PID controller are self-tuning on-line, which improve the performance of controller. Finally, simulation and experimental results show the performance of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.51-55
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• 1995

An adaptive unified predictive control (UPC) algorithm is applied to a batch polymerization reactor for poly(methyl methancrylate) (PMMA) and the effects of controller parameters are investigated. Computational studies are performed for a batch polymerization system model developed in this study. A transfer function in parametric form is estimated by recursive least squares (RLS) method, and the UPC algorithm is implemented to control the reactor temperature on the basis of this transfer function. The adaptive unified predictive controller shows a better performance than the PID controller for tracking set point changes, especially in the latter part of reaction course when gel effect becomes significant. Various performance can be acquired by selecting adequate values for parameters of the adaptive unified predictive controller; in other words, the optimal set of parameters exists for a given set of reaction conditions and control objective.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.8
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• pp.617-628
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• 1989

In systematic tuning of digital STC parameters, systems with time-varying parameters and systems with time-varying delays are studied individually because of many preconditions and difficulties. In order to eliminate all these difficulties effectively, the expert control technique is required to enhance STC control functions. In this paper, an expert controller, a STC utilizing expert control technique, for process control is designed. The expert controller is composed of an unstability indicator for detecting plant unstability, an expert back-up controller and an expert STC which are switched each other by the unstability indication, and expert system with knowledge base and inference engine. This expert controller is able to perform control functions successfuly for the following` 1) a system which has unknown and time-varying delay time, 2) a time-varying system which has unknown parameters, and 3) a system with minimun and non-minimum phase. The robust control function is demonstrated by computer simulations.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.65-75
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• 2004

This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.62-74
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• 2002

The tracking of a reference temperature trajectory in a polymerization batch reactor is a common problem and has critical importance because the quality control of a batch reactor is usually achieved by implementing the trajectory precisely. In this study, only energy balances around a reactor are considered as a design model for control synthesis, and material balances describing concentration variations of involved components are treated as unknown disturbances, of which the effects appear as time-varying parameters in the design model. For the synthesis of a tracking controller, a method combining the input-output linearization of a time-variant system with the parameter estimation is proposed. The parameter estimation method provides parameter estimates such that the estimated outputs asymptotically follow the measured outputs in a specified way. Since other unknown external disturbances or uncertainties can be lumped into existing parameters or considered as another separate parameters, the method is useful in practices exposed to diverse uncertainties and disturbances, and the designed controller becomes robust. And the design procedure and setting of tuning parameters are simple and clear due to the resulted linear design equations. The performances and the effectiveness of the proposed method are demonstrated via simulation studies.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.675-681
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• 1989

As an adaptive control function generator, the CMAC (Cerebellar Model Arithmetic or Articulated Controller) based learning control has drawn a great attention to realize a rather robust real-time manipulator control under the various uncertainties. There remain, however, inherent problems to be solved in the CMAC application to robot motion control or perception of sensory information. To apply the CMAC to the various unmodeled or modeled systems more efficiently, It is necessary to analyze the effects of the CMAC control parameters an the trained net. Although the CMAC control parameters such as size of the quantizing block, learning gain, input offset, and ranges of input variables play a key role in the learning performance and system memory requirement, these have not been fully investigated yet. These parameters should be determined, of course, considering the shape of the desired function to be trained and learning algorithms applied. In this paper, the interrelation of these parameters with learning performance is investigated under the basic learning schemes presented by authors. Since an analytic approach only seems to be very difficult and even impossible for this purpose, various simulations have been performed with prespecified functions and their results were analyzed. A general step following design guide was set up according to the various simulation results.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.2
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• pp.30-37
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• 2012

Pneumatic Active Engine Mount(PAEM) with open-loop control system has been developed to reduce the transmission of the idle-shake vibration induced by engine effectively and economically. A solenoid valve installed between PAEM and vacuum tank is on-off switched by the Pulse Width Modulate(PWM) control signal to decrease the dynamic stiffness of the engine mount. This paper presents the methodology to identify the optimal values of control parameters of a PAEM, i.e, turn-on timing and duty ratio of PWM signal for 6 different idle driving conditions. A scanning algorithm was first applied to the vehicle test to obtain the approximate optimal control parameters minimizing the vibration at front seat rail and at steering wheel. Then the PAEM system identification was fulfilled to find accurate optimal control parameters by using multi-layer neural networks of Levenberg-Marquardt algorithm with vehicle test data.

• 한국기계연구소 소보
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• s.19
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• pp.125-139
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• 1989

As an adaptive control function generator, the CMAC (Cerebellar Model Arithmetic or Articulated Controller) based learning control has drawn a great attention to realize a rather robust real-time manipulator control under the various uncertainties. There remain, however, inherent problems to be solved in the CMAC application to robot motion control or perception of sensory information. To apply the CMAC to the various unmodeled or modeled systems more efficiently, it is necessary to analyze the effects of the CMAC control parameters on the trained net. Although the CMAC control parameters such as size of the quantizing block, learning gain, input offset, and ranges of input variables play a key role in the learning performance and system memory requirement, these have not been fully investigated yet. These parameters should be determined, of course, considering the shape of the desired function to be trained and learning algorithms applied. In this paper, the interrelation of these parameters with learning performance is investigated under the basic learning schemes presented by authors. Since an analytic approach only seems to be very difficult and even impossible for this purpose, various simulations have been performed with pre specified functions and their results were analyzed. A general step following design guide was set up according to the various simulation results.

• The Journal of Korean Physical Therapy
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• v.17 no.4
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• pp.569-574
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• 2005

The purposes of this study were to present the basic reference data of age and specipic gait parameters for Parkinson's disease patients. The basic gait parameters were extracted from 20 patients of parkinson's disease and 20 healthy control subjects using VICON 512 Motion Analyzer. The temporal gait parameters and kinematic parameters is data of Parkinson' s Disease Patients. The results were as follows: (1) In patients' group, cadence, walking velocity were less than control group (p<.05). (2) In patients' group, maximum flexion of hip, maximum adduction of hip and maximum flexion of the knee were less than control group (p<.05). (3) In patients' group, maximum varus of the knee were more than control group (p<.05).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.594-599
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• 2007

A dosed-loop sensorless stroke control system for a linear compressor has been designed. The motor parameters are identified as a function of the piston position and the motor current. They are stored in ROM table and used later for the accurate estimation of piston position. Also it was attempted to approximate the identified motor parameters to the 2nd-order surface functions. The 2nd-order surface functions are divided into 2 or 4 sub-sections for more precise identification of motor parameters. Some experimental results are given in order to show the feasibility of the proposed control schemes for linear compressors.