• Journal of Institute of Control, Robotics and Systems
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• v.11 no.11
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• pp.895-900
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• 2005

We derive a linear matrix inequality(LMI) condition guaranteeing that any invariant zeros of a triple (A, B, C) lie in the open left half plane of the complex plane, i.e. $C(sI-A)^{-1}B$ is minimum phase. The LMI condition is equivalent to a certain constrained Lyapunov matrix equation which can be found in many results relating to stability analysis or control design. We show that the LMI condition can be used to simplify various control engineering problems such as a dynamic output feedback control problem, a variable structure static output feedback control problem, and a nonlinear system observer design problem. Finally, we give some numerical examples.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.329-334
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• 2010

This paper presents the design of a neural network based adaptive control for missile. Acceleration of missile by tail fin control cannot be controllable by DMI (Dynamic Model Inversion) directly because it is non-minimum phase system. To avoid the non-minimum phase system, dynamic model inversion is applied with output-redefinition method. In order to evaluate performance of the suggested controllers we selected the three cases such as control surface fail, control surface loss and wing loss for model uncertainty. The corresponding aerodynamic databases to the failure cases were calculated by using the Missile DATACOM. Using a high fidelity 6DOF simulation program of the missile the performance was evaluates.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.12
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• pp.977-983
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• 2003

An output feedback stabilizing controller far non-minimum phase nonlinear systems is presented. We first perform the standard input-output linearization of the system and then transform the zero dynamics into a special normal form in which the antistable part is not affected by the stable part and the antistable part is given in approximately linear form. Under the assumption that the nonlinear system satisfies the observability rank condition, we can design an observer f3r the extended system that is made of the augmentation of a chain of integrators. The proposed output feedback stabilizing controller can then be designed by combining the observer and the state feedback controller.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1218-1225
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• 2008

This paper presents the design of a neural network based adaptive control for missile is presented. The application model is Exocet MM40, which is derived from missile DATCOM database. Acceleration of missile by tail Fin control cannot be controllable by DMI (Dynamic Model Inversion) directly because it is non-minimum phase system. So, the inner loop consists of DMI and NN (Neural Network) and the outer loop consists of PI controller. In order to satisfy the performances only with PI controller, it is necessary to do some additional process such as gain tuning and scheduling. In this paper, all flight area would be covered by just one PI gains without tuning and scheduling by applying mixture control technique of conventional controller and NN to the outer loop. Also, the simulation model is designed by considering non-minimum phase system and compared the performances to distinguish the validity of control law with conventional PI controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.187-192
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• 2009

This paper presents a method for designing a robust controller that alleviates disturbance effects and compensates performance degradation owing to the time-delay. Disturbance observer(DOB) approach as a tool of robust control has been widely employed in industry. However, since the Pade approximation of time-delay makes the plant non-minimum phase, the classical DOB cannot be applied directly to the system with time-delay. By using a new DOB structure for non-minimum phase systems together with the Smith Predictor, we propose a new controller for reducing the both effects of disturbance and time-delay. Moreover, the closed-loop system can be made robust against uncertain time-delay with the help of a Pill controller tuning method that is based on a second-order plus dead time modeling technique.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.147-150
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• 2001

본 논문은 비최소 위상을 가지는 시스템에 대한 역변환 문제를 실험적으로 고찰, 연구하였다. 일반적으로 선형적이고 인과적인 시스템의 입$cdot$ 출력관계는 행렬형태로 공식화할 수 있다. 최소위상(minimum phase) 시스템의 시스템행렬은 항상 역행렬이 존재하며 안정적이지만 비최소 위상(non-minimum phase)시스템의 시스템행렬은 근사특이(near-singular)행렬 또는 특이(singular) 행렬이므로 불량조건(ill-conditioning)이 발생하고 역변환이 존재할 수 없다. 비최소 위상 시스템의 역변환 문제는 다른 과정을 포함하지 않고서는 인과적이고 안정적인 역변환 필터를 가질 수 없다. 따라서 역변환 필터의 구현을 위해 SVD(singular value decomposition)를 이용하였다. 비최소 위상 시스템인 경우 시스템행렬은 하나이상의 매우 작은 특이 값을 가지며 이것은 시스템의 위상정보를 가진다. 이 성질을 이용하여 시스템의 근사적인 역변환 필터를 구현하고 비최소 위상을 갖는 외팔보에 대해 실험적으로 검증하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• Proceedings of the KIPE Conference
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• 2007.11a
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• pp.148-150
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• 2007

A novel torque sharing function (TSF) is presented. To improve efficiency and to reduce torque ripple in commutation region, only a phase torque under commutation is regulated to produce a uniform torque. And the torque developed by the other phase remains with the previous state under a current limit of the motor and drive. If the minimum change of a phase torque reference can not satisfy the total reference torque, two-phase changing mode is used. Since a phase torque is constant and the other phase torque is changed at each rotor position, total torque error can be reduced within a phase torque error limit. And the total torque error is dependent on the change of phase torque. To consider non-linear torque characteristics and to suppress a tail current at the end of commutation region, the incoming phase current is changed to torque increasing direction, but the outgoing phase current is changed to torque decreasing direction. So, the torque sharing of the outgoing phase and incoming phase can be smoothly changed with a minimum current cross over. The proposed control scheme is verified by some computer simulations and experimental results.

• Proceedings of the KIEE Conference
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• 2007.10c
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• pp.138-140
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• 2007

A novel non-linear logical torque sharing function (TSF) is presented. To improve efficiency and to reduce torque ripple in commutation region, only a phase torque under commutation is regulated to produce a uniform torque. And the torque developed by the other phase remains with the previous state under a current limit of the motor and drive. If the minimum change of a phase torque reference can not satisfy the total reference torque, two-phase changing mode is used. Since a phase torque is constant and the other phase torque is changed at each rotor position, total torque error can be reduced within a phase torque error limit. And the total torque error is dependent on the change of phase torque. To consider non-linear torque characteristics and to suppress a tail current at the end of commutation region, the incoming phase current is changed to torque increasing direction, but the outgoing phase current is changed to torque decreasing direction. So, the torque sharing of the outgoing phase and incoming phase can be smoothly changed with a minimum current cross over. The proposed control scheme is verified by some computer simulations and experimental results.