• Journal of Mechanical Science and Technology
• /
• v.16 no.3
• /
• pp.283-291
• /
• 2002

The paper demonstrates the tracking performance of a sliding horizon feedback/feedforward preview optimal control when applied to a hydraulic motion simulator which has been built to provide a means of replicating the actual ride dynamics of an automobile seat/human system. The design was developed by solving an ordinary differential equation problem instead of a Ricatti equation. Simulation results indicate that the proposed technique has good performance improvement in phase tracking when compared to the classical design methods. It is also found that the controller can be adjusted more easily for robustness due to more tuning parameters.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.49-55
• /
• 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
• /
• 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..

• Journal of the Korean Operations Research and Management Science Society
• /
• v.14 no.2
• /
• pp.67-74
• /
• 1989

This paper considers single-product production and inventory management problem where cumulative demands up to each time period are mutually independent random variables(known) having continuous probability distributions and the associated cost-minimizing production schedule (when to produce and how much to produce) need be determined in rolling horizon environment. For the problem, both the production cost and the inventory holding and backlogging costs are included in the whole system cost. The probability distributions of these costs are expressed in terms of random demands, and utilized to exploit a solution procedure for a production schedule which minimizes the expected unit time system cost and also reduces the probability of rist that, for the first-period of each production cycle (rolling horizon), the cost of the "production" option will exceed that of the "non-production" one. Numerical examples are presented for the solution procedure illustration.cedure illustration.

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

• 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].

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

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1140-1142
• /
• 1996

In this paper, a fixed-horizon $H_{\infty}$ tracking control (HTC) for continuous time-varying systems is proposed in state-feedback case. The solution is obtained via the dynamic game theory. From HTC, an intervalwise receding horizon $H_{\infty}$ tracking control (IHTC) for continuous periodic systems is obtained using the intervalwise strategy. The conditions under which IHTC stabilizes the closed-loop system are proposed. Under proposed stability conditions, it is shown that IHTC guarantees the $H_{\infty}$-norm bound.

• The Bulletin of The Korean Astronomical Society
• /
• v.46 no.2
• /
• pp.45.2-45.2
• /
• 2021

Merging of two supermassive black holes would generate gravitational waves that can be detected by the Pulsar Timing Array (PTA) in the nHz band. In order to assess the plausibility of GW detection with PTA and to develop the data analysis scheme, it is important to understand the underlying properties of black holes and black hole binaries. In this work, we present mass and redshift distributions of black hole mergers using the Horizon Run 5 (HR5) data and discuss their implications for GW detection. We find a general conjecture about the black hole merger tree is true with the Horizon Run 5. For example, a) relatively lighter black holes merge at higher redshifts and b) binary mergers do contribute to the formation of more massive black holes toward low redshifts. We also present our plan to use the black hole properties extracted from the HR5 data in order to generate simulated GW signals to be injected into actual PTA data analysis pipelines. Mass and distance obtained from the HR5 would be key ingredients to generate a more realistic PTA source data set.

• Korea Trade Review
• /
• v.47 no.5
• /
• pp.21-37
• /
• 2022

Baltic Dry Index (BDI) is difficult to forecast because of the high volatility and complexity. To improve the BDI forecasting ability, this study apply Bayesian variable selection method with a large number of predictors. Our estimation results based on the BDI and all predictors from January 2000 to September 2021 indicate that the out-of-sample prediction ability of the ADL model with the variable selection is superior to that of the AR model in terms of point and density forecasting. We also find that critical predictors for the BDI change over forecasts horizon. The lagged BDI are being selected as an key predictor at all forecasts horizon, but commodity price, the clarksea index, and interest rates have additional information to predict BDI at mid-term horizon. This implies that time variations of predictors should be considered to predict the BDI.