Nonlinear Predictive Control on a Heterogeneous Computing Platform
For embedded control systems, this work enables more efficient nonlinear model predictive control by leveraging heterogeneous computing, though it is an incremental hardware implementation.
This paper presents an interior-point-based nonlinear predictive controller on a heterogeneous CPU-FPGA platform, achieving 18x memory savings and 36x speedup over an ARM Cortex-A9 software implementation.
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 field-programmable 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.