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Using evolved controllers to adapt behavior in autonomous nonlinear systems

The proportional-integrative-derivative (PID) controller provides effective control in linear systems. However, performance is poor in nonlinear systems unless combined with a fuzzy logic controller (FLC) that modifies the PID controller gains as needed. System behavior can adapt to operational environment changes by switching different FLCs online. But that capability requires accurate operational environment identification. In autonomous systems identification must be done without significant human presence. This paper describes how Discrete Fourier Transforms can identify operational environments in an autonomous nonlinear system, which in this work is a DC motor. Simulation results shows the proposed method accurately identifies the physical environment and altering the FLC online correctly adapts the system's behavior.