• Journal of Institute of Control, Robotics and Systems
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• v.9 no.3
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• pp.177-185
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• 2003

Current issues of receding horizon control scheme are reviewed. The basic idea of receding horizon control is presented first. For unconstrained and constrained systems, the results of closed-loop stability in receding horizon control are surveyed. We investigate the two categories of robustness of receding horizon control : stability robustness and performance robustness. The existing optimization algorithm to solve receding horizon control problem is briefly mentioned. It is shown that receding horizon control has been extended to nonlinear systems without losing good properties such as stability and robustness. Many industrial applications are reported along with extensive references related to receding horizon control.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.801-806
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• 1987

The receding horizon tracking control for the discrete time invariant systems is presented in this paper. This control law is derived with the receding horizon concept from the standard tracking problems. Stability properties of this control law are analyzed. It is shown that there exists a finite horizon index for which the closed loop systems are always asymptotically stable. The receding horizon tracking control is a kind of predictive control and will add a new clan to many existing predictive controls, with which some comparisons are made.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.460-465
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• 2004

In this paper, we present some properties on receding horizon $H_{\infty}$ control for nonlinear discrete-time systems. First, we propose the nonlinear inequality condition on the terminal cost for nonlinear discrete-time systems. Under this condition, noninceasing monotonicity of the saddle point value of the finite horizon dynamic game is shown to be guaranteed. We show that the derived condition on the terminal cost ensures the closed-loop internal stability. The proposed receding horizon $H_{\infty}$ control guarantees the infinite horizon $H_{\infty}$ norm bound of the closed-loop systems. Also, using this cost monotonicity condition, we can guarantee the asymptotic infinite horizon optimality of the receding horizon value function. With the additional condition, the global result and the input-to-state stable property of the receding horizon value function are also given. Finally, we derive the stability margin for the saddle point value based receding horizon controller. The proposed result has a larger stability region than the existing inverse optimality based results.

• 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.

• 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.

• 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.

• 전기의세계
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• v.28 no.5
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• pp.44-48
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• 1979

For ordinary systems the receding horizon method has beer proved by the author as a very useful and easy tool to find stable feedback controls. In this paper an open-loop optimal control which minimizes the control energy with a suitable upper limit and terminal control and state constraints is derived and then transformed to the closed-loop control. The stable feedback control law is obtained from the closed-loop control. The stable feedback control law is obtained from the closed-loop control by the receding horizon concept. It is shown by the Lyapunov method that the control law derived from the receding, horizon concept is asymtotically stable under the complete controllability condition. The stable feedback control which is similar to but more general than the receding horizon control is presented in this paper To the author's knowledge the control laws in this paper are easiest to stabilize systems with delay in control.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.535-535
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• 2000

This paper presents an adaptive receding horizon H$_{\infty}$ controller for the linear parameter varying systems in the deterministic environment, which combines a parameter range estimator and a robust receding horizon H$_{\infty}$ controller using the parameter bounds. Using parameter set inclusion and terminal inequality condition, the closed-loop system stability is guaranteed. It is shown that the stabilizing adaptive receding horizon H$_{\infty}$ controller guarantees the H$_{\infty}$ norm bound.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.15-15
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• 2003

We provide some recent results of approximation algorithms for solving Markov Games and discuss their applications to problems that arise in Computer Science. We consider a receding horizon approach as an approximate solution to two-person zero-sum Markov games with an infinite horizon discounted cost criterion. We present error bounds from the optimal equilibrium value of the game when both players take “correlated” receding horizon policies that are based on exact or approximate solutions of receding finite horizon subgames. Motivated by the worst-case optimal control of queueing systems by Altman, we then analyze error bounds when the minimizer plays the (approximate) receding horizon control and the maximizer plays the worst case policy. We give two heuristic examples of the approximate receding horizon control. We extend “parallel rollout” and “hindsight optimization” into the Markov game setting within the framework of the approximate receding horizon approach and analyze their performances. From the parallel rollout approach, the minimizing player seeks to combine dynamically multiple heuristic policies in a set to improve the performances of all of the heuristic policies simultaneously under the guess that the maximizing player has chosen a fixed worst-case policy. Given $\varepsilon$>0, we give the value of the receding horizon which guarantees that the parallel rollout policy with the horizon played by the minimizer “dominates” any heuristic policy in the set by $\varepsilon$, From the hindsight optimization approach, the minimizing player makes a decision based on his expected optimal hindsight performance over a finite horizon. We finally discuss practical implementations of the receding horizon approaches via simulation and applications.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.429-432
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• 2004

In this paper, we derive the guaranteed bound of the horizon size for the stabilizing receding horizon control(RHC). From the convergence property of the solution to the Riccati equation, it is shown that the lower bound can be represented in terms of the parameters in the given system model, which makes an off-line calculation possible. Additionally, it is shown to be able to obtain the stabilizing RHC without respect to the final weighting matrix. The proposed guaranteed bound is obtained numerically via simulation.