• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.455-457
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• 1998

This paper designs a noise controller for the small cavity using Coefficient Diagram Method(CDM). In the small cavity system, there exist nonlinear characteristics such as uncertain-time delay and parameter variation. In the controller design of nonlinear system with uncertainty need to the higher order controller or complexity computation. The coefficient diagram is convenient implementation of the control system design method, that is utilized as a vehicle to collectively express the important features of the system and an improved version Kessler's standard form and the Lipatov stability condition of a constitutes the theoretical basis. Simultaneously, it is provided a desired specification, such as the robustness, the stability, faster response, and lower order controller. A simulation of the system with the proposed controller shows sufficient noise cancelation in small cavity.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.82-89
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• 1997

In this paper, an adaptive speed controller with an FNN(Feedforward Neural Network) is proposed for servo motor drives. Generally, the motor system has nonlinearities in friction, load disturbance and magnetic saturation. It is necessary to treat the nonlinearities for improving performance in servo control. The FNN can be applied to control and identify a nonlinear dynamical system by learning capability. In this study, at first, a robust speed controller is developed by Lyapunov stability theory. However, the control input has discontinuity which generates an inherent chattering. To solve the problem and to improve the performances, the FNN is introduced to convert the discontinuous input to continuous one in error boundary. The FNN is applied to identify the inverse dynamics of the motor and to control the motor using coordination of feedforward control combined with inverse motor dynamics identification. The proposed controller is developed for an SR motor which has highly nonlinear characteristics and it is compared with an MRAC(Model Reference Adaptive Controller). Experiments on an SR motor illustrate te validity of the proposed controller.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.2017-2024
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• 2020

In this study, a fractional order proportional integral derivative (FOPID) controller is designed to create the reference power trajectory and to conquer the uncertainties and external disturbances. A fractional nonlinear model was utilized to describe the nuclear reactor dynamic behaviour considering thermal-hydraulic effects. The controller parameters were tuned using optimization method in Matlab/Simulink. The FOPID controller was simulated using Matlab/Simulink and the controller performance was evaluated for Hard variation of the reference power and compared with that of integer order a proportional integral derivative (IOPID) controller by two models of fractional neutron point kinetic (FNPK) and classical neutron point kinetic (CNPK). Also, the FOPID controller robustness was appraised against the external disturbance and uncertainties. Simulation results showed that the FOPID controller has the faster response of the control attempt signal and the smaller tracking error with respect to the IOPID in tracking the reference power trajectory. In addition, the results demonstrated the ability of FOPID controller in disturbance rejection and exhibited the good robustness of controller against uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.144-149
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• 1993

This paper discusses a design of a nonlinear control for a class of single-input double-output nonlinear mechanical systems. When conventional linearization methods are applied to the mechanical systems, some problems of oscillation and unstable phenomena arise. The proposed nonlinear control system resolves these problems. In this design the eigenvalues of the closed-loop nonlinear system are assigned to desired locations and local asymptotic stability of the closed-loop system. is guaranteed. The design method is applied to an inverted pendulum system with a moving weight mechanism. Experimental results show that the proposed nonlinear controller is more effective for stability than the usual linear controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1165-1168
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Network(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Radial Basis Function Networks(RBFN). The learning with RBFN is fast and precise. This paper discusses RBFN as identification procedure is based on a nonlinear dynamical systems. and A design method of model follow control system based on RBFN controller is developed. As a result of applying this method to inverted pendulum, the simulation has shown that RBFN can be used as identification and control of nonlinear dynamical systems effectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.9
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• pp.1795-1805
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• 2002

A position controller which can achieve a specified dynamic performance irrespective of the different operating position of the pneumatic cylinder is proposed. The position controller developed in this paper is composed of a nonlinear compensator and a disturbance observer. The nonlinear compensator which feeds back position, velocity and acceleration is derived from the nonlinear dominating equations of the position control system to compensate for variation of dynamic characteristics of a pneumatic cylinder according to the change of the operating position. The disturbance observer including a simplified linear model is designed to reduce the effect of model discrepancy in the low frequency range which cannot be suppressed by the nonlinear compensator. The results of the experiments show that the position control performance maintains a designed performance regardless of the variations of an operating position of the pneumatic cylinder.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.7
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• pp.605-613
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• 2001

In this paper, we design a robust adaptive controller for a nonlinear system with uncertainties to be rejected via disturbance adaptation law. The nonlinear system considered has unknown nonlinear functions being influenced by external disturbance. The upper bound of unknown nonlinear functions at each time is estimated by using a disturbance adaptation law. The estimated nonlinear functions are used to design a stabilizing function a control input. Tuning function is used to estimates unknown system parameter without overparametrization. A set-point regulation error converges to a residual set close to zero asymptotically. The effectiveness of the proposed controller is investigated by computer simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.76-82
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• 2009

A Heating, Ventilating and Air Conditioning (HVAC) system is a nonlinear multi-input multi-output (MIMO) system. This system is very difficult to control the temperature and the humidity ratio of a thermal space because of complex nonlinear characteristics. This paper proposes an adaptive fuzzy output feedback control based on observer for the nonlinear HVAC system. The nonlinear HVAC system is linearized through dynamic extension. State observers are designed for estimating state variables of the HVAC system. Fuzzy systems are employed to approximate uncertain nonlinear functions of the HVAC system with unavailable state variables. The obtained controller compares with an adaptive feedback controller. Simulation is given to demonstrate the effectiveness of our proposed adaptive fuzzy method.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1134-1136
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• 1996

A necessary and suficient conditions is proposed for feedback linearizable SISO systems with unknown constant parameters. It is shown that the systems which satisfy the proposed conditions can be transformed into a controllable linear system with unknown parameter and it can be stabilized using the nonlinear feedback linearizing controller. We also present the analysis and implementation of a nonlinear feedback linearizing control for an Electro-Magnetic Suspension (EMS) system. We show that an EMS system is nonlinear feedback linearizable and satisfies the proposed conditions, and hence that the proposed nonlinear feedback controller for an EMS system is robust against mass parameter perturbation and force disturbance.