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An integrated perturbation analysis and Sequential Quadratic Programming approach for Model Predictive Control
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DOI：10.1016/j.automatica.2009.06.028.png)
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Computationally efficient algorithms are critical in making Model Predictive Control (MPC) applicable to broader classes of systems with fast dynamics and limited computational resources. In this paper, we propose an integrated formulation of Perturbation Analysis and Sequential Quadratic Programming (InPA-SQP) to address the constrained optimal control problems. The proposed algorithm combines the complementary features of perturbation analysis and SQP in a single unified framework, thereby leading to improved computational efficiency and convergence property. A numerical example is reported to illustrate the proposed method and its computational effectiveness. (C) 2009 Elsevier Ltd. All rights reserved.
Keyword:
Nonlinear MPC
Fast MPC
Neighboring Extremal Optimal Control
Sequential Quadratic Programming
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期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
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Explicit sub-optimal linear quadratic regulation with state and input constraints.png)
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