Nonlinear predictive control on a heterogeneous computing platform

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Title: Nonlinear predictive control on a heterogeneous computing platform
Author(s): Khusainov, B
Kerrigan, EC
Suardi, A
Constantinides, GA
Item Type: Conference Paper
Abstract: Nonlinear Model Predictive Control (NMPC) is an advanced control technique that often relies on computationally demanding optimization and integration algorithms. This paper proposes and investigates a heterogeneous hardware implementation of an NMPC controller based on an interior point algorithm. The proposed implementation provides flexibility of splitting the workload between a general-purpose CPU with a fixed architecture and a field-programmable gate array (FPGA) to trade off contradicting design objectives, namely performance and computational resource usage. A new way of exploiting the structure of the Karush-Kuhn-Tucker (KKT) matrix yields significant memory savings, which is crucial for reconfigurable hardware. For the considered case study, a 10x memory savings compared to existing approaches and a 10x speedup over a software implementation are reported. The proposed implementation can be tested from Matlab using a new release of the Protoip software tool, which is another contribution of the paper. Protoip abstracts many low-level details of heterogeneous hardware programming and allows quick prototyping and processor-in-the-loop verification of heterogeneous hardware implementations.
Publication Date: 9-Jul-2017
Date of Acceptance: 27-Feb-2017
Publisher: IFAC
Copyright Statement: This paper is embargoed until publication.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: PITN-GA-2013-607957
Conference Name: IFAC World Congress 2017
Publication Status: Accepted
Start Date: 2017-07-09
Finish Date: 2017-07-14
Conference Place: Toulouse, France
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering

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