• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.12
• /
• pp.1183-1187
• /
• 2011

In this paper, an optimal FIR (Finite-Impulse-Response) filter is proposed for discrete time-varying state-space models. The proposed filter estimates the current state using measured output samples on the recent time horizon so that the variance of the estimation error is minimized. It is designed to be linear, unbiased, with an FIR structure, and is independent of any state information. Due to its FIR structure, the proposed filter is believed to be robust for modeling uncertainty or numerical errors than other IIR filters, such as the Kalman filter. For a general system with system and measurement noise, the proposed filter is derived without any artificial assumptions such as the nonsingular assumption of the system matrix A and any infinite covariance of the initial state. A numerical example show that the proposed FIR filter has better performance than the Kalman filter based on the IIR (Infinite- Impulse-Response) structure when modeling uncertainties exist.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.16 no.1
• /
• pp.135-146
• /
• 1991

This paper shows the infinite horizon model of Partially Observable Markov Decision Process with lagged information. The lagged information is uncertain delayed observation of the process under control. Even though the optimal policy of the model exists, finding the optimal policy is very time consuming. Thus, the aim of this study is to find an .eplison.-optimal stationary policy minimizing the expected discounted total cost of the model. .EPSILON.- optimal policy is found by using a modified version of the well known policy iteration algorithm. The modification focuses to the value determination routine of the algorithm. Some properties of the approximation functions for the expected discounted cost of a stationary policy are presented. The expected discounted cost of a stationary policy is approximated based on these properties. A numerical example is also shown.

• Journal of applied mathematics & informatics
• /
• v.26 no.3_4
• /
• pp.615-625
• /
• 2008

A single item order level inventory model for perishable products is considered in which a constant fraction of on hand inventory spoils per unit time. Demand linearly depends on time. The fluctuation of demand is taken into account to determine minimum total cost of the system. Both discrete and continuous fluctuations are considered. The model is developed and solved analytically for infinite time horizon. A numerical example is presented for finite time horizon. Sensitivity analysis of the model is carried out.

• Transactions on Control, Automation and Systems Engineering
• /
• v.3 no.2
• /
• pp.83-88
• /
• 2001

The robust linear H(sub)$\infty$ FIR filter, which guarantees a prescribed H(sub)$\infty$ performance, is designed for continuous time-varying systems with unknown cone-bounded nonlinearity. The infinite horizon filtering for time-varying systems is systems is investigated in therms of two Riccati equations by the finite moving horizon.

• International Journal of Reliability and Applications
• /
• v.12 no.2
• /
• pp.131-143
• /
• 2011

In most of literatures of age replacement policy, the authors consider the case that a new item starts operating at time zero and is to be replaced by new one at time T. It is, however, often to purchase used items because of the limited budget. In this paper, we consider age replacement policy of a used item whose age is $t_0$. The mathematical formulas of the expected cost rate per unit time are derived for both infinite-horizon case and finite-horizon case. For each case, we show that the optimal replacement age exists and is finite and investigate the effect of the age of the used item.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.424-427
• /
• 1995

In this paper, a control law based on the receding horizon concept which robustly stabilizes time-varying discrete linear systems, is proposed. A dynamic game problem minimizing the worst case performance, is adopted as an optimization problem which should be resolved at every current time. The objective of the proposed control law is to guarantee the closed loop stability and the infinite horizon $H^{\infty}$ norm bound. It is shown that the objective can be achieved by selecting the proper terminal weighting matrices which satisfy the inequality conditions proposed in this paper. An example is included to illustrate the results..

• 전기의세계
• /
• v.49 no.9
• /
• pp.9-16
• /
• 2000

A receding horizon $H_{\infty}$ predictive control method is derived by solving a min-max problem in non-recursive forms. The min-max cost index is converted to a quadratic form which for systems with input saturation can be minimized using QP. Through the use of closed-loop prediction the prediction of states the use of closed-loop prediction the prediction of states in the presence of disturbances are made non-conservative and it become possible to get a tighter $H_{\infty}$ norm bound. Stability conditions and $H_{\infty}$ norm bounds on disturbance rejection are obtained in infinite horizon sence. Polyhedral types of feasible sets for sets and disturbances are adopted to deal with the input constraints. The weight selection procedures are given in terms of LMIs and the algorithm is formulated so that it can be solved via QP. This work is a modified version of an earlier work which was based on ellipsoidal type feasible sets[15].

• Journal of applied mathematics & informatics
• /
• v.24 no.1_2
• /
• pp.49-64
• /
• 2007

In this paper, we investigate optimal policies for three age-dependent populations in a competition system, which is controlled by fertility. By using Dubovitskii-Milyutin's general theory, the maximum principles are obtained for problems with free terminal states, infinite horizon and target sets, respectively.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1901-1904
• /
• 2003

MPC or model predictive control is representative of control methods which are able to handle physical constraints. Closed-loop stability can therefore be ensured only locally in the presence of constraints of this type. However, if the system is neutrally stable, and if the constraints are imposed only on the input, global aymptotic stability can be obtained; until recently, use of infinite horizons was thought to be inevitable in this case. A globally stabilizing finite-horizon MPC has lately been suggested for neutrally stable continuous-time systems using a non-quadratic terminal cost which consists of cubic as well as quadratic functions of the state. The idea originates from the so-called small gain control, where the global stability is proven using a non-quadratic Lyapunov function. The newly developed finite-horizon MPC employs the same form of Lyapunov function as the terminal cost, thereby leading to global asymptotic stability. A discrete-time version of this finite-horizon MPC is presented here. The proposed MPC algorithm is also coded using an SQP (Sequential Quadratic Programming) algorithm, and simulation results are given to show the effectiveness of the method.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.193-196
• /
• 1992

When there exist parameter uncertainty, modelling errors and nonminimum phase zeros in control object system. the stability robustness of conventional LQG and LOG/LTR methods are not satisfactory[2, 8]. Since these methods are performed on the infinite horizon, it is very hard to establish exact design parameters and thus they have lots of problems to be applied to real systems, So in this paper we propose RHLQG/FIRF optimal controller which has robust stability against parameter uncertainty, nonminimum phase zeros and modelling errors. This method uses only the information around at present and therefore shows good performance even when we do not know exact design parameters. We here compare LQG and LQG/LTR method with RHLQG/FIRF controller and exemplify that RHLQG/FIRF controller has better robust stability performance via simulations.