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Fast Model Predictive Control Using Online Optimization

Wang, Yang, Boyd, Stephen·2010·IEEE Transactions on Control Systems Technology

Abstract

A widely recognized shortcoming of model predictive control (MPC) is that it can usually only be used in applications with slow dynamics, where the sample time is measured in seconds or minutes. A well-known technique for implementing fast MPC is to compute the entire control law offline, in which case the online controller can be implemented as a lookup table. This method works well for systems with small state and input dimensions (say, no more than five), few constraints, and short time horizons. In this paper, we describe a collection of methods for improving the speed of MPC, using online optimization. These custom methods, which exploit the particular structure of the MPC problem, can compute the control action on the order of 100 times faster than a method that uses a generic optimizer. As an example, our method computes the control actions for a problem with 12 states, 3 controls, and horizon of 30 time steps (which entails solving a quadratic program with 450 variables and 1284 constraints) in around 5 ms, allowing MPC to be carried out at 200 Hz.

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Metadata

Authors
Wang, Y., & Boyd, S.
Venue
IEEE Transactions on Control Systems Technology
Published
2010-03
Volume
18
Issue
2
Pages
267-278
ISSN
  • 1063-6536 (ISSN-L)
  • 1558-0865
OpenAlex
W2116773335
Cited by
1,372
References
40
FWCI
91.39
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Fast Model Predictive Control Using Online Optimization. https://app.cochat.ai/papers/10.1109/tcst.2009.2017934
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Wang, Y., & Boyd, S. (2010). Fast Model Predictive Control Using Online Optimization. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2009.2017934

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