• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.19-26
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• 2024

In recent years, an increasing number of experimental studies have shown that the practical application of mature active control systems requires consideration of robustness criteria in the design process, including the reduction of tracking errors, operational resistance to external disturbances, and measurement noise, as well as robustness and stability. Good uncertainty prediction is thus proposed to solve problems caused by poor parameter selection and to remove the effects of dynamic coupling between degrees of freedom (DOF) in nonlinear systems. To overcome the stability problem, this study develops an advanced adaptive predictive fuzzy controller, which not only solves the programming problem of determining system stability but also uses the law of linear matrix inequality (LMI) to modify the fuzzy problem. The following parameters are used to manipulate the fuzzy controller of the robotic system to improve its control performance. The simulations for system uncertainty in the controller design emphasized the use of acceleration feedback for practical reasons. The simulation results also show that the proposed H∞ controller has excellent performance and reliability, and the effectiveness of the LMI-based method is also recognized. Therefore, this dynamic control method is suitable for seismic protection of civil buildings. The objectives of this document are access to adequate, safe, and affordable housing and basic services, promotion of inclusive and sustainable urbanization, implementation of sustainable disaster-resilient construction, sustainable planning, and sustainable management of human settlements. Simulation results of linear and non-linear structures demonstrate the ability of this method to identify structures and their changes due to damage. Therefore, with the continuous development of artificial intelligence and fuzzy theory, it seems that this goal will be achieved in the near future.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.4
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• pp.833-845
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• 2001

Quantitative Feedback Theory(QFT) has been used to design a robust power system stabilizer(PSS) to improve transient and dynamic stabilities of a power system. This design technique is basically accomplished in frequency domain. The most important feature of QFT is that it is able to deal with the design problem of complicated uncertain plants. A basic idea in QFT design is the translation of closed-loop frequency-domain specifications into Nichols chart domains specifying the allowable range of the nominal open-loop response and then to design a controller by using the gain-phase loop shaping technique. This paper introduces a new algorithm to compute QFT bounds more efficiently. The propose QFT design method ensures a satisfactory performance of the PSS under a wide range of power system operating conditions.

• Journal of Power Electronics
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• v.17 no.1
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• pp.181-189
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• 2017

The three-phase four-leg voltage-source inverter topology is an interesting option for the three-phase four-wire system. With an additional leg, this topology can achieve superior performance under unbalanced and nonlinear load conditions. However, because of the low bandwidth of conventional controllers in high-power inverter applications, the system cannot guarantee a balanced output voltage under the unbalanced load condition. Most of the methods proposed to solve this problem mainly use the multiple synchronous frame method, which requires several controllers and a large amount of computation because of frame transformation. This study proposes a simple hybrid controller that combines proportional-integral (PI) and resonant controllers in the synchronous frame synchronized with the positive-sequence component of the output voltage of the three-phase four-leg inverter. The design procedure for the controller and the theoretical analysis are presented. The performance of the proposed method is verified by the experimental results and compared with that of the conventional PI controller.

• Journal of Mechanical Science and Technology
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• v.18 no.12
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• pp.2080-2094
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• 2004

A model predictive controller is designed to control thermal power in a nuclear reactor. The basic concept of the model predictive control is to solve an optimization problem for finite future time steps at current time, to implement only the first optimal control input among the solved control inputs, and to repeat the procedure at each subsequent instant. A controller design model used for designing the model predictive controller is estimated every time step by applying a recursive parameter estimation algorithm. A 3-dimensional nuclear reactor analysis code, MASTER that was developed by Korea Atomic Energy Research Institute (KAERI), was used to verify the proposed controller for a nuclear reactor. It was known that the nuclear power controlled by the proposed controller well tracks the desired power level and the desired axial power distribution.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.187-192
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• 2004

In this paper, we proposed the method to design fuzzy controller using the experience of the operating expert and experimental numeric data for the robust control about the noise and disturbance instead of the traditional PID controller for the main steam temperature control of the thermal power plant. The temperature of main steam temperature process has to be controlled uniformly for the stable electric power output. The process has the problem of the hunting for the cases of various disturbances. In that case, the manual action of the operator happened to be introduced in some cases. We adopted the TSK (Takagi-Sugeno-Kang) model as the fuzzy controller and designed the fuzzy rules using the informations extracted directly from the real plant and various operating condition to solve the above problems and to apply practically. We implemented the real fuzzy controller as the Function Block module in the DCS(Distributed Control System) and evaluated the feasibility through the experimental results of the simulation.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.307-310
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• 2006

We consider the problem of constructing observers for nonlinear systems with unknown inputs. It is shown that under some conditions, there exists an observer estimating the states of nonlinear systems with unknown inputs. Nonlinear observer design method using observer error linearization and the design technique of unknown input observer(UIO) for linear systems are used to derive conditions. Some illustrative examples are included. In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller.The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.606-608
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• 1997

In this paper, Neuro-Fuzzy Controller(NFC), a fuzzy system realized using a neural network, is to adopt for the multivariable system. In the multivariable system, the interactive effects between the variables should be taken into account. A simple compensator, using the steady-state information can be obtained for open-loop stable systems, is presented to cope with this problem. However, it should be supposed that the plant is unknown to the control system designer, but an estimate of the DC gain has been obtained by carrying out experiments on the plant. Also, if the variables are not combinated completely, it is difficult to design the controller. Therefore, we design a neuro-fuzzy controller which controls a multivariable system with only input output informations, and compare its performance with that of a PI controller. In the proposed controller, the construction of the membership functions and rule base, which is highly heuristic, can be achieved using a training process. This allows the combination of knowledge of human experts and evidence from input-output data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.191-194
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• 2000

In this paper, an output tracking control technique of discrete-time Takagi-Sugeno (TS) fuzzy systems is developed. The TS fuzzy system is represented as an uncertain multiple linear system. The tracking problem of TS fuzzy system is converted into the stabilization problem of a uncertain multiple linear system. A sufficient condition for asymptotic tracking is obtained in terms of linear matrix inequalities (LMI). A design example is illustrated to show the effectiveness of the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.501-504
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• 1995

A Simultaneous optimal design of structural and control system of a semi-active suspension is applied on a helf-car model in this paper. Suspension stiffnesses and dampings are selected as structural design parameters and damping forces of variable dampers as controller parameters. Sence this optimization problem is of large discontinuous space, conventional exhaustive methods are not enough. So we here try out an approach using Genetic Algorithm for our problem. Through numerical simulation work, the performance of the simultaneously optimized system was tested and showed meaningful improvement over the partially optimized ones.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.591-593
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• 1999

The finite time optimum regulation problem of a discrete time bilinear system with a quadratic performance criterion is obtained in terms of a sequence discrete algebraic Lyapunov equations. Our new method is based on the successive approximations. This algorithm saves the computation time to solve the optimal problem, and the design procedure is illustrated for an example.