• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.282-288
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• 2003

It is known that HIV (Human Immunodeficiency Virus) infection, which causes AIDS after some latent period, is a dynamic process that can be modeled mathematically. Effects of available anti-viral drugs, which prevent HIV from infecting healthy cells, can also be included in the model. In this paper we illustrate control theory can be applied to a model of HIV infection. In particular, the drug dose is regarded as control input and the goal is to excite an immune response so that the symptom of infected patient should not be developed into AIDS. Finite horizon optimal control is employed to obtain the optimal schedule of drug dose since the model is highly nonlinear and we want maximum performance for enhancing the immune response. From the simulation studies, we found that gradual reduction of drug dose is important for the optimality. We also demonstrate the obtained open-loop optimal control is vulnerable to parameter variation of the model and measurement noise. To overcome this difficulty, we finally present nonlinear receding horizon control to incorporate feedback in the drug treatment.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1951-1956
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• 2003

It is known that HIV (Human Immunodeficiency Virus) infection, which causes AIDS after some latent period, is a dynamic process that can be modeled mathematically. Effects of available anti-viral drugs, which prevent HIV from infecting healthy cells, can also be included in the model. In this paper we illustrate control theory can be applied to a model of HIV infection. In particular, the drug dose is regarded as control input and the goal is to excite an immune response so that the symptom of infected patient should not be developed into AIDS. Finite horizon optimal control is employed to obtain the optimal schedule of drug dose since the model is highly nonlinear and we want maximum performance for enhancing the immune response. From the simulation studies, we find that gradual reduction of drug dose is important for the optimality. We also demonstrate the obtained open-loop optimal control is vulnerable to parameter variation of the model and measurement noise. To overcome this difficulty, we finally present nonlinear receding horizon control to incorporate feedback in the drug treatment.

• Advances in robotics research
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• v.1 no.1
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• pp.41-59
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• 2014

This paper deals with the problem of steering a group of mobile robots along a reference path while maintaining a desired geometric formation. To solve this problem, the overall formation is decomposed into numerous geometric patterns composed of pairs of robots, and the state of the geometric patterns is defined. A control algorithm for the problem is proposed based on the Nash equilibrium strategies incorporating receding horizon control (RHC), also known as model predictive control (MPC). Each robot calculates a control input over a finite prediction horizon and transmits this control input to its neighbor. Considering the motion of the other robots in the prediction horizon, each robot calculates the optimal control strategy to achieve its goals: tracking a reference path and maintaining a desired formation. The performance of the proposed algorithm is validated using numerical simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.27.3-27
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• 2001

In this paper, a robust receding horizon controller for uncertain linear time-varying systems is presented in the dynamic output-feedback form. The existing output-feedback receding horizon controller in the literature is composed of a state observer and a static controller associated with the observer states (similar to LQC control), where the fundamental assumption is that the state observer will supply the exact states as time goes up. The performance of those controllers may be much degraded and even the closed-loop stability may not be guaranteed when the system suffers from disturbances and uncertainties or is time-varying. The proposed controller, which is not necessary to have the state-observer, overcomes such difficulties. Using matrix inequality conditions on the terminal weighting matrix, the closed-loop system stability is guaranteed. Numerical examples are ...

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1911-1916
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• 2003

Parameter set of an LPV system is divided into a number of subsets so that robust feedback gains may be designed for each subset of parameters. A concept of quasi-invariant set is introduced, which allows finite steps of delay in reentrance to the set. A feasible and positively invariant set with respect to a gain-scheduled state feedback control can be easily obtained from the quasi-invariant set. A receding horizon control strategy can be derived based on this feasible and invariant set.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.307-308
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• 2006

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.567-573
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• 1998

본 논문에서는 입력과 상태변수에 hard constraint가 있는 이산시간 시스템에 대한 새로운 이동구간 제어기를 제안한다. 제안된 이동구간제어기가 terminal ellipsoid constraint를 이용하여 지수 안정성(exponential stability)을 보장함을 보인다. Feasibility를 증가시키기 위하여 시스템을 안정 모드와 불안정 모드로 바꾸는 방법, constraint를 완화하는 방법을 제안한다. Constraint를 완화할 때 constraint를 만족시키지 못하는 부분의 성능을 개선시키는 방법을 제안하고 예제를 통해서 몇 가지 제안한 방법들을 비교한다.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• 전기의세계
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• v.49 no.9
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• pp.17-24
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• 2000

This paper presents sufficient conditions7 for monotonicity of the saddle point value for receding-horizon H$\infty$ control(RHHC). The resulting monotonicity is used to prove the stability of the closed-loop. Under these sufficient conditions the well-known terminal equality condition is handled as a special case and the condition on the state weighting matrix is weakened so as to include even the zero matrix. The whole procedure is much simpler than the previous results and thus is expected to be easily extended for constrained delayed and/or nonlinear systems with the RHHC.

• Advances in robotics research
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• v.2 no.2
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• pp.161-182
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• 2018

This paper proposes a novel receding horizon control (RHC) algorithm for formation control of a swarm of unmanned surface vehicles (USVs) using particle swarm optimization (PSO). The proposed control algorithm provides the coordinated path tracking of multi-agent USVs while preventing collisions and considering external disturbances such as ocean currents. A three degrees-of-freedom kinematic model of the USV is used for the RHC with guaranteed stability and convergence by incorporating a sequential Monte Carlo (SMC)-based particle initialization. An ocean current model-based estimator is designed to compensate for the effect of ocean currents on the USVs. This method is compared with the PSO-based RHC algorithms to demonstrate the performance of the formation control and the collision avoidance in the presence of ocean currents through numerical simulations.