• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.991-997
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• 2002

The concept of feasible & invariant region plays an important role to derive closed loop stability and achie adequate performance of constrained receding horizon predictive control. In this paper, we define a complex polyhedral feasible & invariant set for all stabilizable input-constrained linear systems by using a complex transform and propose a one-norm based receding horizon control scheme using these invariant sets. In order to get a larger stabilizable set, a convex hull of invariant sets which are defined for different state feedback gains is used as a target invariant set of the constrained receding horizon control. The proposed constrained receding horizon control scheme is formulated so that it can be solved via linear programming.

• Asian Journal of Turfgrass Science
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• v.7 no.2_3
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• pp.55-60
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• 1993

Soil profile was well developed into four horizons, $A_1$, $A_3$, $B_2$and C at 100cm-depth in most four golf courses in Kyonggi province. Distribution of root system of Korean lawngrass was abundant in dark yellowish or yellowish brown $A_1$ horizon with low hardness(8~14mm yamanaka scale), moderately in yellowish brown $A_3$ horizon with moderate hardness(16~23mm) rarely in $B_3$horizon(15~60cm depth) and no in C horizon. Optimum soil hardness for good root growth of Korean lawngrass appeared to be less than 16mm mineral nutrient contents. Such as Ca++, Mg++, K+, Mn++ and Fe showed relatively higher concentration in lower horizon indicating the leaching of minerals. The increasing tendency of soil pH with depth seemed to the result of mineral leaching. There was significant positive correlation between Ca+Mg and pH, manganese content appeared to be too high(261~789ppm) in $A_1$ horizon. The contents of organic matter and phosphorus were bight in $A_1$ horizon and greatly varied among golf courses.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.9
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• pp.1039-1045
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• 1988

In the original incremental generalized predictive control, the receding horizon predictive control is introduced as a control law. But in this paper, we propose a generalized predictive self-tuning control using full-valued incremental controls. The control law is a mean horizon predictive control. The effectiveness of this algorithm in a variable time delay or load disturbances environment is demonstrated by computer simulation. The controlled plant is a nonminimum phase system.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.279-282
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• 1995

In this paper, a moving horizon control algorithm, which can be applied for a wide class of nonlinear systems with control and state constraints, is considered. In a neighborhood of the origin, a linear feedback controller is applied. Outside this neighborhood, a moving horizon control law is applied. The time taken to solve an optimal control problem is considered in the algorithm so that the proposed control law can be applied as an on-line controller.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.430-433
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• 1997

In this paper, an intervalwise receding horizon control (IRHC) is proposed which stabilizes linear continuous and discrete time-varying systems each other by means of a feedback control stemming from a receding horizon concept and a minimum quadratic cost. The results parallel those obtained for continuous [4],[9] and discrete time varying system [5],[15] each other.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.159.1-159
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• 2001

A new time-domain FIR parameter estimation called the receding horizon least square estimation (RHLSE) is suggested for stochastic systems by combining the well known least square estimation with the receding horizon strategy. It can be always obtained without the requirement of any \textit{a priori} information about the horizon initial parameter. A fast algorithm for the suggested estimation is also presented which is remarkable in the view of computational advantage and simple implementation. It is shown that the proposed estimation is robust against temporary modeling uncertainties due to their FIR structure through simulation studies.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.6
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• pp.614-628
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• 1988

In the original incremental Extended Horizon Control, the control inputs are computed recursively each step in the prediction horizon. But in this paper, we propose another incremental Extended Horizon Self-tuning Control version in which control inputs can be computed directly in any time interval. The effectiveness of this algorithm in a variable time delay or load disturbances environment is demonstrated by computer simulation. The controlled plant is a nonminimum phase system.

• Journal of Communications and Networks
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• v.16 no.3
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• pp.293-300
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• 2014

As energy harvesting communication systems emerge, there is a need for transmission schemes that dynamically adapt to the energy harvesting process. In this paper, after exhibiting a finite-horizon online throughput-maximizing scheduling problem formulation and the structure of its optimal solution within a dynamic programming formulation, a low complexity online scheduling policy is proposed. The policy exploits the existence of thresholds for choosing rate and power levels as a function of stored energy, harvest state and time until the end of the horizon. The policy, which is based on computing an expected threshold, performs close to optimal on a wide range of example energy harvest patterns. Moreover, it achieves higher throughput values for a given delay, than throughput-optimal online policies developed based on infinite-horizon formulations in recent literature. The solution is extended to include ergodic time-varying (fading) channels, and a corresponding low complexity policy is proposed and evaluated for this case as well.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.815-823
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• 1999

This study considers an implementation of artificial neural networks to the receding horizon optimal control and is applications to power systems. The Generalized Backpropagation-Through-Time (GBTT) algorithm is presented to deal with a quadratic cost function defined in a finite-time horizon. A decentralized approach is used to control the complex global system with simpler local controllers that need only local information. A Neural network based Receding horizon Optimal Control (NROC) 1aw is derived for the local nonlinear systems. The proposed NROC scheme is implemented with two artificial neural networks, Identification Neural Network (IDNN) and Optimal Control Neural Network (OCNN). The proposed NROC is applied to a power system to improve the damping of the low-frequency oscillation. The simulation results show that the NROC based power system stabilizer performs well with good damping for different loading conditions and fault types.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.163-170
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• 2003

The optimal Bayesian filter for a single target is known to provide the best tracking performance in a cluttered environment. However, its main drawback is the increase in memory size and computation quantity over time. In this paper, the inevitable predicament of the optimal Bayesian filter is resolved in a suboptimal fashion through the use of a receding horizon strategy. As a result, the problems of memory and computational requirements are diminished. As a priori information, the horizon initial state is estimated from the validated measurements on the receding horizon. Consequently, the suboptimal algorithm proposed allows for real time implementation.