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Nonlinear Model Predictive Control via FeasibilityPerturbed Sequential Quadratic Programming
 UNIVERSITY OF WISCONSINMADISON, COMPUTER SCIENCES DEPARTMENT
, 2002
"... Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trustregion feasibilityperturbed sequential quadratic programming algorithm (developed in a companion report), then ..."
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Cited by 16 (3 self)
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Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trustregion feasibilityperturbed sequential quadratic programming algorithm (developed in a companion report), then discuss its adaptation to the problems arising in nonlinear model predictive control. Computational experience with several representative sample problems is described, demonstrating the e#ectiveness of the proposed approach.
unknown title
, 2010
"... Optimal control of twocommercial aircraft dynamic system during approach. The noise levels minimization ..."
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Optimal control of twocommercial aircraft dynamic system during approach. The noise levels minimization
The Trust Region Sequential Quadratic Programming method applied to twoaircraft acoustic optimal control problem
"... Abstract: This paper aims to reduce noise levels of twoaircraft landing simultaneously on approach. Constraints related to stability, performance and ight safety are taken into account. The problem of optimal control is described and solved by a Sequential Quadratic Programming numerical method &ap ..."
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Abstract: This paper aims to reduce noise levels of twoaircraft landing simultaneously on approach. Constraints related to stability, performance and ight safety are taken into account. The problem of optimal control is described and solved by a Sequential Quadratic Programming numerical method 'SQP ' when globalized by the trust region method. By using a merit function, a sequential quadratic programming method associated with global trust regions bypasses the nonconvex problem. This method used a nonlinear interior point trust region optimization solver under AMPL. Among several possible solutions, it is shown that there is an optimal trajectory leading to a reduction of noise levels on approach.
unknown title
, 2007
"... sequential quadratically constrained quadratic programming method with an augmented Lagrangian line search function ..."
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sequential quadratically constrained quadratic programming method with an augmented Lagrangian line search function
We cons...
, 2009
"... This paper considers the connection between the intrinsic Riemannian Newton method and other more classically inspired optimization algorithms for equalityconstrained optimization problems. We consider the feasiblyprojected sequential quadratic programming (FPSQP) method and show that it yields ..."
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This paper considers the connection between the intrinsic Riemannian Newton method and other more classically inspired optimization algorithms for equalityconstrained optimization problems. We consider the feasiblyprojected sequential quadratic programming (FPSQP) method and show that it yields the same update step as the Riemannian Newton, subject to a minor assumption on the choice of multiplier vector. We also consider Newton update steps computed in various ‘natural ’ local coordinate systems on the constraint manifold and find simple conditions that guarantee that the update step is the Riemannian Newton update. In particular, we show that this is the case for projective local coordinates, one of the most natural choices that have been proposed in the literature. Finally we consider the case where the full constraints are approximated to simplify the computation of the update step. We show that if this approximation is good at least to secondorder then the resulting update step is the Riemannian Newton update. The conclusion of this study is that the intrinsic Riemannian Newton algorithm is the archetypal feasible second order update for nondegenerate equality constrained optimisation problems. Key words. Feasiblyprojected sequential quadratic programming (FPSQP); equalityconstrained optimization; Riemannian manifold; Riemannian Newton method; sequential Newton method; retraction; osculating paraboloid; secondorder correction; second funda