• 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.

• 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.

• 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.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.475-484
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• 2004

In this paper, we present a dynamic output-feedback receding horizon $H_{\infty}$controller for linear discrete-time systems with disturbance. The controller is obtained numerically from the finite horizon output-feedback $H_{\infty}$optimization problem, which is, in fact, hardly solved analytically. Under a matrix inequality condition on the terminal weighting matrix, the monotonic decreasing property of the cost is shown. This property guarantees both the closed-loop stability and the $H_{\infty}$norm bound. Then, we extend the proposed design method to a reference tracking problem and a problem for time-varying systems. Numerical examples are given to illustrate the performance of the proposed controller.

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

• 전기의세계
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• v.49 no.9
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• pp.9-16
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• 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].

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.556-559
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• 1996

In this paper, we suggest a H center .inf. generalized predictive control(H center GPC) which guarantees $H_{\infty}$-norm bounds. THe suggested control is obtained by solving the min-max problem in nonrecursive forms. The stability conditions of the suggested control are derived in a somewhat simple form and it is not required for the derived solution to be a saddle point solution. It is also shown that the suggested control guarantees the $H_{\infty}$-norm bounds under the same conditions of stability.

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

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.529-534
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• 2005

In this paper, a new type of output feedback control, called a $H_2/H_{\infty}$ fnite memory control (FMC), is proposed for deterministic state space systems. Constraints such as linearity, unbiasedness property, and finite memory structure with respect to an input and an output are required in advance to design $H_2/H_{\infty}$ FMC in addition to the performance criteria in both $H_2$ and $H_{\infty}$ sense. It is shown that $H_2$, $H_{\infty}$, and mixed $H_2/H_{\infty}$ FMC design problems can be converted into convex programming problems written in terms of linear matrix inequalities (LMIs) with some linear equality constraints. Through simulation study, it is illustrated that the proposed $H_2/H_{\infty}$ FMC is more robust against uncertainties and faster in convergence than the existing $H_2/H_{\infty}$ output feedback control schemes.