• 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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• 1992.10b
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• pp.49-55
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• 1992

In this paper, we developed a Receding Horizon Predictive Control for Stochastic state space models(RHPCS). RHPCS was designed to minimize a quadratic cost function. RHPCS consists of Receding Horizon Tracking Control(RHTC) and a state observer. It was shown that RHPCS is equivalent to Generalized Predictive Control(GPC) when the underlying state space model is equivalent to the I/O model used in the design of GPC. The equivalence between GPC and RHPCS was shown through. the comparison of the transfer functions of the two controllers. RHPCS provides a time-invarient optimal control law for systems for which GPC can not be used. The stability properties of RHPCS was derived. From the GPC's equivalence to RHPCS, the stability properties of GPC were shown to be the same as those for RHTC.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.502-502
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• 2000

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. For constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time Instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an of set error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.777-780
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• 1995

Accounting for actuator nonlinearities in control loops has often been perceived as an implementation issue and usually excluded in the design of controllers. Nonlinearities treated in this paper are saturation, and they are modelled as an inequality constraint. The CRHPC(Constrained Receding Horizon Predictive Control) with inequality constraints algorithm is used to handle actuator rate and amplitude limits simultaneously or respectively. Optimum values of future control signals are obtained by quadratic programming. Simulated examples show that predictive control law with inequality constraints offers good performance as compared with input clipping.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2548-2551
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• 2005

In this paper, a robust receding horizon finite impulse response(FIR) filter is proposed for a class of linear discrete time systems with uncertainty satisfying an integral quadratic constraint. The robust state estimation problem involves constructing the set of all possible states at the current time consistent with given system input, output measurements and the integral quadratic constraint.

• 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.