• Earthquakes and Structures
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• v.12 no.3
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• pp.285-296
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• 2017

Base isolation is a quite practical control strategy for enhancing the response of structural systems induced by strong ground motions. Due to the dynamic effects of base isolation systems, reduction in the interstory drifts of the superstructure is often achieved at the expense of high base displacement level, which may lead to instability of the structure or non-practical designs for the base isolators. To reduce the base displacement, several hybrid structural control strategies have been studied over the past decades. This study investigates a particular strategy that employs Tuned Mass Dampers (TMDs) for improving the performance of base-isolated structures and unlike previous studies, specifically focuses on the effectiveness of this hybrid control strategy in structures that are equipped with nonlinear base isolation systems. To consider the nonlinearities of base isolation systems, a Bouc-Wen model is selected and nonlinear dynamic OpenSees models are used to perform several time-history simulations in time and frequency domains. Through these numerical simulations, the effects of several parameters such as the fundamental period of the structure, dynamic properties of the TMD and isolation systems and properties of the input ground motion on the behaviour of TMD-structure-base isolation systems are examined. The results of this study provide a better insight into the performance of linear shear-story structures with nonlinear base isolators and show that there are many scenarios in which TMDs can still improve the performance of these systems.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.138-146
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• 2007

The analysis and design method for achieving robust stabilization of Decentralized Dynamic Surface Control (DDSC) is presented for a class of interconnected nonlinear systems. While a centralized design approach of DSC was developed in [1], the decentralized approach to deal with large-scale interconnected systems is proposed under the assumption that interconnected functions among subsystems are unknown but bounded. To provide a closed-loop form with provable stability properties, augmented error dynamics for N nonlinear subsystems with DDSC are derived. Then, the reachable set for errors of the closed-loop systems will be approximated numerically in the form of an ellipsoid in the framework of convex optimization. Finally, a numerical algorithm to calculate the $L_2$ gain of the augmented error dynamics is presented.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.471-476
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• 2001

This paper presents a state observer for nonlinear systems using approximate observer from. It is shown that if a nonlinear system is approximately error linearizable, then there exists a local nonlinear observer whose estimation error converges exponentially to zero. Since the proposed method relaxes strong geometric conditions of previous works, it improves the existing results for nonlinear observer design. Finally, some example is given to show the effectiveness of this scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.339-345
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• 2006

An analysis methodology of Decentralized Dynamic Surface Control (DDSC) for the large-scale interconnected nonlinear systems is presented in this paper. While the centralized DSC approach proposed in [14] has a difficulty to check the quadratic stability for the large-scale systems numerically due to dramatic increases of the order of overall augmented error dynamics, DDSC is relatively easy to check the quadratic stability since lower order error dynamics of individual subsystems are used. Then, a systematic procedure for designing DDSC will be developed. Furthermore, after a quadratic function containing a reachable set is defined, it will be calculated numerically to indicate the performance of DDSC in the framework of convex optimization. Finally an illustrative example will be given for showing the advantages of DDSC compared with other decentralized nonlinear control techniques.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.47-50
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• 2003

For large-scale systems which are composed of interconnections of many lower-dimensional subsystems, decentralized control is preferable since it can alleviate the computational burden, avoid communication between different subsystems, and make the control more feasible and simpler. A power system is such a large-scale system where generators are interconnected through transmission lines. Decentralized control is therefore considered for power systems. In this paper, a robust decentralized excitation control scheme for interactions is proposed to enhance the transient stability of multimachine power systems. First we employ a DFL(Direct Feedback Linearization) compensator to rancel most of the nonlinearities; however, the resulting model still contains nonlinear interconnections. Therefore, we design a robust controller in order to deal with Interconnection terms. In this procedure, an upper bound of interconnection terms is estimated by an estimator. The resulting adaptive scheme guarantees the uniform ultimate boundedness of the closed-loop dynamic systems in the presence of the uncertainties.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.453-456
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• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. In order to evaluate the performance of our controller design method, two direct adaptive control methods are applied to a Duffing's equation and a Lorenz equation which are continuous-time chaotic systems. Our simulation results show the effectiveness of the controllers.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.404-408
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• 1986

While the engine dynamics is, in nature, a uncertain nonlinear system, practical control methods for nonlinear systems are presently limited. This paper discusses important issues of engine control such as fuel economy, exhaust emission control, and driveability. Recent development in engine control technologies is reviewed. New control methods which appear to be useful for engine control are introduced.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.259-267
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• 2003

In this paper, we propose a nonlinear variable PID controller using a cell-mediated immune response. An immune feedback response is based on the functioning of biological T-cells. An immune feedback response and P-controller of conventional PID controllers resemble each other in role and mechanism. Therefore, we extend immune feedback mechanism to nonlinear PE controller. And in order to choose the optimal nonlinear PID controller games, we also propose the on-line tuning algorithm of nonlinear functions parameters in immune feedback mechanism. The trained parameters of nonlinear functions are adapted to the variations of the system parameters and any command velocity. And the adapted parameters obtained outputs of nonlinear functions with an optimal control performance. To verify performances of the proposed control systems, the speed control of nonlinear BC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system variations.

• Structural Engineering and Mechanics
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• v.31 no.5
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• pp.621-624
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• 2009

The time delay in active and semi-active controls is an important research subject. Many researches on the time-delay control for deterministic systems have been made (Hu and Wang 2002, Yang et al. 1990, Abdel-Mooty and Roorda 1991, Pu 1998, Cai and Huang 2002), while the study on that for stochastic systems is very limited. The effects of the time delay on the control of nonlinear systems under Gaussian white noise excitations have been studied by Bilello et al. (2002). The controlled linear systems with deterministic and random time delay subjected to Gaussian white noise excitations have been treated by Grigoriu (1997). Recently, a stochastic averaging method for quasi-integrable Hamiltonian systems with time delay has been proposed (Liu and Zhu 2007). In the present paper, a stochastic optimal time-delay control method for stochastically excited nonlinear structural systems is proposed based on the stochastic averaging method for quasi Hamiltonian systems with time delay and the stochastic dynamical programming principle. An example of stochastically excited and controlled hysteretic column is given to illustrate the proposed control method.