A SVM optimization tool and FPGA system architecture applied to NMPC

Abstract

Support Vector Machines (SVMs) are supervised learning models of the machine learning field whose performance strongly depended on its hyperparameters. The Bio-inspired Optimization Tool for SVM (BIOTS) tool is based on a Multi-Objective Particle Swarm Algorithm (MOPSO) to tune hyperparameters of SVMs. In this work, BIOTS is proposed along with a custom hardware design generator (VHDL) that implements the SVM in a Field-Programmable Gate Array (FPGA). Both tools are combined to create an approximate Nonlinear Model Predictive Controller (NMPC) applied to a single-link robotic arm. The result is a generated SVM implemented in a FPGA yielding better results in terms of speed and simplicity compared to our previous work that addressed the same problem with a Radial Basis Functions Neural Networks (RBFNN).