Abstract
This paper investigates application of SQP optimization algorithms to nonlinear model predictive control. It considers feasible vs. infeasible path methods, sequential vs. simultaneous methods and reduced vs. full space methods. The suitable choices between these various strategies are assessed informally through a case study.
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