• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.1-7
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• 2008

This paper presents a method to design a repetitive controller that is robust to variations in the system parameters. The uncertain parameters are specified probabilistically by their probability distribution functions. Instead of working with the distribution functions directly, the repetitive controller is designed from a set of models that are generated from the specified probability functions. With this multiple-model design approach, any number of uncertain parameters that follow any type of distribution functions can be treated. furthermore, the controller is derived by minimizing a frequency-domain based cost function that produces monotonic convergence of the tracking error as a function of repetition number. Numerical illustrations show how the proposed multiple-model design method produces a repetitive controller that is significantly more robust than an optimal repetitive controller designed from a single nominal model of the system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.217-217
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• 2000

A decentralized robust EA(eigensoucture assignment) controller is designed for an active suspension system of a vehicle based on a full car model with 7-degree of freedom. Using overlapping decomposition, the full car model is decentralized by two half car models. For each half car model, a robust eigenstructure assignment controller can be obtained by using optimization approach. The performance of the decentralized robust EA controller is compared with that of a conventional centralized EA controller through computer simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2069-2074
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• 2016

This paper presents a methodology based on evolutionary optimization for simultaneously optimizing design parameters of controller and components of plant. Genetic programming(GP) based bond graph model generation is adopted to open-ended search for the plant. Also GP is applied to represent the controller with a unified method. The formulations of simultaneous plant-controller design optimization problem and the description of solution techniques based on bond graph are derived. A feasible solutions for a plant/controller design using the simultaneous optimization methodology is illustrated.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.528-530
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• 1998

This paper addresses analysis and design of a fuzzy model-based-controller for the control of uncertain SISO nonlinear systems. In the design procedure, we represent the nonlinear system by using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller via parallel distributed compensation and sliding mode control. Unlike other parallel distributed controllers. this globally stable fuzzy controller is designed without finding a common positive definite matrix for a set of Lyapunov equations, and has good tracking performance. Furthermore, stability analysis is conducted not for the fuzzy model but for the real underlying nonlinear system. A simulation is included for the control of the Duffing forced-oscillation system, to show the effectiveness and feasibility of the proposed fuzzy control method.

• Nuclear Engineering and Technology
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• v.38 no.6
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• pp.551-560
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• 2006

In this study, a controller for a nuclear reactor power is designed. The reactor is modeled using the three dimensional reactor design code MASTER. From the relationship of the input and output of the reactor code, a reactor dynamic model is derived by the system identification method. This model is more realistic than the one based on mathematical theories. With this model, a robust controller is designed by the extended frequency response method. As this method has the same theoretical background as the classical method, all of the existing design techniques of the classical method can be used directly. Furthermore, by introducing the real part of a Laplacian operator into the frequency response, the control design specification can be considered at the initial stage of design. The designed controller is simple, and gives a sufficient robustness with good performance.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.501-508
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• 2000

It is demanded to find the dynamic model of a real structure to design a controller. However, as the structure has inherently infinite number of degree-of-freedom, it is impossible to obtain an exact dynamic model of the structure. Instead a reduction model with finite degree-of-freedom is used for the design of a controller. So there exists uncertainty between a real model and a reduction model which causes poor performance of control. All these uncertainties can degrade the control performance and even cause the control instability. Thus, robust control strategy considering the above uncertainties can be an alternative one to guarantee the performance and stability of the control. This study deals with the experimental verification of robust controller design for the active mass driver. $\mu$-synthesis technique is employed as a robust control strategy. Some weights are chosen based on the difference between the initial plant with which the controller is designed and the perturbed plant to be controlled having the actuator uncertainty. The robustness of $\mu$-synthesis technique is compared with the result of LQG strategy, which does not consider the uncertainty.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.11
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• pp.9-21
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• 2009

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.506-514
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• 2008

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

• Transactions of The Korea Fluid Power Systems Society
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• v.2 no.4
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• pp.7-13
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• 2005

This paper presents modeling and ostimator/controller design for the hydraulic system in Vehicle Stability Control(VSC) system. A nonlinear mathematical model of the VSC hydraulic system is proposed and its accuracy is experimentally verified. A brake pressure estimator is then designed based on the derived mathematical model of VSC hydraulic system. And a disturbance observer, which compensates the estimation error between the brake pressure and the computed brake pressure is also designed to enhance the accuracy of the estimator. The proposed controller has the form of a feedback controller and determines explicitly the on/off ratio of valves' driving PWM signals by means of making use of the simplified mathematical model in the VSC hydraulic system. The performance of the designed controller whose feedback signal is generated by the brake pressure estimator is validated through experimental results.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1146-1155
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• 2017

Normally, the artificial intelligence algorithms are widely applied to the optimal controller design. Then, it is expected that the best output performance is achieved. Unfortunately, when resulting controller parameters are implemented by using the practical devices, the output performance cannot be the best as expected. Therefore, the paper presents the optimal controller design using the combination between the state-space averaging model and the adaptive Tabu search algorithm with the new criteria as two penalty conditions to handle the mentioned problem. The buck-boost converter regulated by the cascade PI controllers is used as the example power system. The results show that the output performance is better than those from the conventional design method for both input and load variations. Moreover, it is confirmed that the reported controllers can be implemented using the realistic devices without the limitation and the stable operation is also guaranteed. The results are also validated by the simulation using the topology model of MATLAB and also experimentally verified by the testing rig.