Automatic Software and Computing Hardware Codesign for Predictive Control

doi:10.1109/TCST.2018.2855666

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Automatic Software and Computing Hardware Codesign for Predictive Control

2019-09-01

B. G. Khusainov

Eric C. Kerrigan *

Constantinides, George A.

DOI：10.1109/TCST.2018.2855666

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Abstract

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Model predictive control (MPC) is a computationally demanding control technique that allows dealing with multiple-input and multiple-output systems while handling constraints in a systematic way. The necessity of solving an optimization problem at every sampling instant often 1) limits the application scope to slow dynamical systems and/or 2) results in expensive computational hardware implementations. Traditional MPC design is based on the manual tuning of software and computational hardware design parameters, which leads to suboptimal implementations. This brief proposes a framework for automating the MPC software and computational hardware codesign while achieving an optimal tradeoff between computational resource usage and controller performance. The proposed approach is based on using a biobjective optimization algorithm, namely BiMADS. Two test studies are considered: a central processing unit and field-programmable gate array implementations of fast gradient-based MPC. Numerical experiments show that the optimization-based design outperforms Latin hypercube sampling, a statistical sampling-based design exploration technique.

Keywords:

Design automation

field-programmable gate array (FPGA)

hardware-software codesign

model predictive control (MPC)

multiobjective optimization (MOO)

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