Nonlinear predictive control on a heterogeneous computing platform. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Khusainov, Bulat; Kerrigan, Eric C.; Suardi, Andrea; Constantinides, George A.
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 11877
• Page End: 11882
• Publication Date: 2017-07
• Subjects: Nonlinear predictive control -- Hardware-software co-design -- Scheduling; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.1413
• Languages: English
• ISSNs: 2405-8963

Abstract

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.