A Self-Tuning Analog Proportional-Integral-Derivative (PID) Controller

We present a platform for implementing low power selftuning analog proportional-integral-derivative controllers. By using a model-free tuning method, the platform overcomes problems typically associated with reconfigurable analog arrays. Unlike a self-tuning digital PID controller, our prototype controller combines the advantages of low power, no quantization noise, high bandwidth and high speed. The prototype hardware uses a commercially available field programmable analog array and Particle Swarm Optimization as the tuning method. We show that a selftuned analog PID controller can outperform a hand-tuned solution and demonstrate adaptability to plant drift.