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Optimal feedback scheduling of model predictive controllers

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Abstract

Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FS-CBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiaotong University, Shanghai, 200030, China

    Pingfang Zhou, Jianying Xie & Xiaolong Deng

Authors
  1. Pingfang Zhou
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  2. Jianying Xie
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  3. Xiaolong Deng
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Additional information

This work was supported by National Science Foundation of China (No. 50405017).

Pingfang ZHOU was born in 1976. He is a Ph.D. student in the department of automation, Shanghai Jiaotong University, Shanghai. His current research interests include real-time systems, computer controlled systems and industrial network.

Jianying XIE was born in 1940. He is a Ph.D. supervisor in the department of automation, Shanghai Jiaotong University, Shanghai. His research interests include networked control systems and computer controlled systems.

Xiaolong DENG was born in 1972. He is a Ph.D. student in the department of automation, Shanghai Jiaotong University, Shanghai. His current research interests include target tracking and optimum estimation.

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Zhou, P., Xie, J. & Deng, X. Optimal feedback scheduling of model predictive controllers. J. Control Theory Appl. 4, 175–180 (2006). https://doi.org/10.1007/s11768-006-5169-1

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  • DOI: https://doi.org/10.1007/s11768-006-5169-1

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