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Linear quadratic cost functions are used as performance indicators.
The latter result is new even when the cost is exactly the quadratic cost.
This is achieved by replacing the purely quadratic cost function employed within the MPC optimization by a mixed linear-quadratic cost function.
A quadratic cost function is considered as a performance measure for the given closed-loop system.
A quadratic cost function is considered as a performance measure for the closed-loop system.
Optimality is given by a minimization problem of a quadratic cost functional under infinite horizon.
A linear quadratic cost function is considered as the performance index of the closed-loop system.
For this, we formulate a quadratic cost function for the nonlinear affine plant.
The control objective is represented by the finite horizon linear quadratic cost functional.
We also establish global W2,p estimates when the cost is a small perturbation of the quadratic cost.
The problem requires the minimization of a manipulability-related quadratic cost function subject to workspace boundary constraints.
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