Performance bounds for robust quadratic dynamic matrix control with end condition

Vuthandam, P.; Genceli, Hasmet; Nikolaou, Michael

doi:10.1002/aic.690410908

Cited by 33 publications

(18 citation statements)

References 8 publications

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“…straints, provides an alternative solution. Fletcher 1987 provides laou and coworkers discuss soft state constraints using l and 1 quadratic-stage cost penalties in DMC with a terminal stabil-Ž ity constraint Genceli and Nikolaou, 1993;Vuthandam et al, . 1995 . Because the soft-constraint MPC objective we use effectively penalizes the 1-norm of constraint violations when s) 0, it can be viewed as an l penalty function for the hard-con-1 straint problem.…”

Section: Soft Constraintsmentioning

confidence: 99%

Feasibility issues in linear model predictive control

1999

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Section: Soft Constraintsmentioning

confidence: 99%

Feasibility issues in linear model predictive control

1999

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“…(37) and (38) Following the preceding observations, it should be noted that the widespread practice of using a discount factor β may be more problematic than realized, in the sense that it may not result in robustly stabilizing strategies. This situation, namely the need to shape weights of the terms in the MPC objective in an increasing rather than decreasing fashion in order to ensure robustness, has been rigorously analyzed in the past (Genceli and Nikolaou, 1993;Vuthandam et al, 1995) and should be explored further.…”

Section: Taylor Rules and Resulting Closed-loop Stabilitymentioning

confidence: 99%

“…The associated Table 15 shows the resulting coefficient for the Taylor-like solution provided by MPC. Second, it has been rigorously shown that keeping m small improves the robustness of the closed loop, namely it helps maintain closed-loop stability in the presence of discrepancies between the model used by MPC and the actual system under control (Garcia and Morari, 1982;Genceli and Nikolaou, 1993;Vuthandam et al, 1995).…”

Section: A Choice Of Prediction Horizon Length Nmentioning

confidence: 99%

Optimal Rules for Central Bank Interest Rates Subject to Zero Lower Bound

Singh

Nikolaou

2014

Economics

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The celebrated Taylor rule provides a simple formula that aims to capture how the central bank interest rate is adjusted as a linear function of inflation and output gap. However, the rule does not take explicitly into account the zero lower bound on the interest rate. Prior studies on interest rate selection subject to the zero lower bound have not produced derivations of explicit formulas. In this work, Taylor-like rules for central bank interest rates bounded below by zero are derived rigorously using a multi-parametric model predictive control framework. This framework is used to derive rules with or without inertia. The proposed approach is illustrated through simulations. Application of the approach to US economy data demonstrates its relevance and provides insight into the objectives underlying central bank interest rate decisions. A number of issues for future study are proposed. JEL E52 C61

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“…Ž . Vuthandam et al 1995 proposed an alternative approach Ž . called EQDMC QDMC with end condition .…”

Section: Cutler and Ramaker 1979 Recognized This And Chose To Posementioning

confidence: 99%