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Linear programming and model predictive control
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DOI：10.1016/S0959-1524(99)00034-7.png)
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Abstract
En 中文
The practicality of model predictive control (MPC) is partially limited by the ability to solve optimization problems in real time. This requirement limits the viability of MPC as a control strategy for large scale processes. One strategy for improving the computational performance is to formulate MPC using a linear program. While the linear programming formulation seems appealing from a numerical standpoint, the controller does not necessarily yield good closed-loop performance. In this work, we explore MPC with an II performance criterion. We demonstrate how the non-smoothness of the objective function may yield either dead-beat or idle control performance. (C) 2000 IFAC. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
model predictive control
linear programming
optimization
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3.9
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3.5K
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9.2K
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