Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees
American Journal of Computer Science and Technology
Volume 1, Issue 1, March 2018, Pages: 19-23
Received: Nov. 21, 2017;
Accepted: Nov. 29, 2017;
Published: Dec. 24, 2017
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Jamal Arezoo, Department of Automation and Instrumentation, Petroleum University of Technology, Ahvaz, Iran
Karim Salahshoor, Department of Automation and Instrumentation, Petroleum University of Technology, Ahvaz, Iran
The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite practical obstacles in implementation of explicit model predictive control (MPC), the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action are removed. This paper addresses complexity of explicit model predictive control (MPC) in terms of online evaluation and memory requirement. Complexity reduction approaches for explicit MPC has recently been emerged as techniques to enhance applicability of MPC. Individual deployment of the approaches has not had enough effect on complexity reduction. In this paper, merging the approaches based on complexity reduction is addressed. The binary search tree and complexity reduction via separation are efficient methods which can be confined to small problems, but merging them can result in significant effect and expansion of its applicability. The simulation tests show proposed approach significantly outperforms previous methods.
Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees, American Journal of Computer Science and Technology.
Vol. 1, No. 1,
2018, pp. 19-23.
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