FPGA Based Adaptive Neuro Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems
The conventional controller like the PID controller requires an exact mathematical model of the controlled system to meet as much control objectives as possible. If it is difficult to establish the mathematical model for a system, the fuzzy logic controller is a good option to achieve a robust controller. Fuzzy logic systems, which can reason with imprecise information, are good at explaining their decisions but they cannot automatically acquire the rules used to make those decisions. On the other hand, artificial neural networks are good at recognizing patterns, and have ability to train the parameters of a control system, but they are not good at explaining how they reach their decisions.