Nonlinear predictive control on a heterogeneous computing platform

Abstract

We propose an implementation of an interior-point-based nonlinear predictive controller on a heterogeneous processor. The workload can be split between a general-purpose CPU and a fieldprogrammable gate array to trade off the contradicting design objectives of control performance and computational resource usage. A new way of exploiting the structure of the KKT matrix yields significant memory savings. We report an 18x memory saving, compared to existing approaches, and a 36x speedup over a software implementation with an ARM Cortex-A9 processor. We also introduce a new release of Protoip, which abstracts low-level details of heterogeneous programming and allows processor-in-the-loop verification.

Automatic Software and Computing Hardware Codesign for Predictive Control

“…For optimization-based control applications, the FPGA circuit has to be verified in the loop with a plant model. In this work we automate the above design flow by using Protoip software tool [16].…”

“…• There is a free software application that implements the algorithm, namely NOMAD [28] • NOMAD provides various interfaces, including a Matlab frontend, which simplifies integration with Protoip [16]. The application scope of BiMADS is limited to bi-objective problems, which can be considered as the main drawback of the algorithm.…”