Automatic control, the process of measuring, computing, and applying an input to control the behaviour of a physical system, is ubiquitous in engineering and industry. Model predictive control (MPC) is an advanced control technology that has been very successful in the chemical process industries due to its ability to handle large multiple input multiple output (MIMO) systems with physical constraints. It has recently been proposed to be applied to higher bandwidth systems, which add the requirement of greater sampling frequencies. The main hurdle is the need to solve a computationally intensive quadratic programming (QP) problem in real-time. In this paper we address the need for acceleration by proposing a highly efficient floating-point field-programmable gate array (FPGA) implementation that exploits the parallelism opportunities offered by interior-point optimization methods. The approach yields a 5x improvement in latency and a 40x improvement in throughput for large problems over a software implementation. This work builds on a previous FPGA implementation of an iterative linear solver, an operation at the heart of the interior-point method.
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