Abstract
Efficient algorithms for constrained minimization of infinite horizon predictive control costs are presented. Control laws with guaranteed stability and asymptotic tracking are derived on the basis of appropriately chosen one-dimensional and ellipsoidal constraint set approximations. These achieve comparable performance with existing QP-based control laws with significant reductions in computational burden.
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