• 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.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2081-2086
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• 2005

This paper proposes the receding horizon $H_{\infty}$ predictive control (RHHPC) for systems with a state-delay. We first proposes a new cost function for a finite horizon dynamic game problem. The proposed cost function includes two terminal weighting terns, each of which is parameterized by a positive definite matrix, called a terminal weighting matrix. Secondly, we derive the RHHPC from the solution to the finite dynamic game problem. Thirdly, we propose an LMI condition under which the saddle point value satisfies the well-known nonincreasing monotonicity. Finally, we shows the asymptotic stability and $H_{\infty}$-norm boundedness of the closed-loop system controlled by the proposed RHHPC. Through a numerical example, we show that the proposed RHHC is stabilizing and satisfies the infinite horizon $H_{\infty}$-norm bound.

• 전기의세계
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• v.49 no.9
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• pp.17-24
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• 2000

This paper presents sufficient conditions7 for monotonicity of the saddle point value for receding-horizon H$\infty$ control(RHHC). The resulting monotonicity is used to prove the stability of the closed-loop. Under these sufficient conditions the well-known terminal equality condition is handled as a special case and the condition on the state weighting matrix is weakened so as to include even the zero matrix. The whole procedure is much simpler than the previous results and thus is expected to be easily extended for constrained delayed and/or nonlinear systems with the RHHC.