Controller fixed-point optimization with genetic algorithms

This work describes a way to optimize the controller fixed-point representation in programmable logic devices (eg. FPGA) with genetic algorithms. The optimization uses the error between floating-point and fixed-point representation as well as a quantization noise error model. Thus, both terms allow weighting between the to be expected theoretical and actually occurred simulation error. This task could be automated easily due to the script features of the simulation system