Hw-Sw codesign of a flexible neural controller through a FPGA-based neural network programmed in VHDL
Eros Gian Alessandro Pasero, Matteo Perri · 2005
Artificial neural networks are extensively applied to several applications where data-driven methods are requested. This work describes a neural architecture, which controls an "inverted pendulum" in a very flexible manner with a reusability perspective. The project was implemented through a "digital core" constituted of a FPGA, a microcontroller and an SRAM block, which co-operate to the neural computation. The FPGA was programmed in VHDL to implement the neural architecture. The core was written in a recursive manner to permit the reconfigurability of the network and its reusability to all the systems, which can be modelled through a similar neural network. Through these parameters the system combines the configurability (typical of a sw project) with the velocity guaranteed by the hw implementation of the mathematical algorithms. Experimental results validated the effectiveness of the proposed approach; the network was able to balance a mechanical inverted pendulum above the middle of the slide guides.