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 singlelink 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).