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A new optimization algorithm with application to nonlinear MPC
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DOI：10.1016/j.jprocont.2004.02.007.png)
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Abstract
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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. A new optimization algorithm coined rFOPT which remains feasibile with respect to inequality constraints is introduced. The suitable choices between these various strategies are assessed informally through a small CSTR case study. The case study also considers the effect various discretization methods have on the optimization problem. (C) 2004 Elsevier Ltd. All rights reserved.
Keywords:
model predictive control
optimization
SQP
feasibility
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