We present a framework for a low power self-tuning analog proportional-integral-derivative controller. By using a model-free tuning method, it overcomes problems associated with reconfigurable analog arrays. In comparison to a self-tuning digital PID controller, it combines the advantages of low power, no quantization noise, high bandwidth and high speed. Our prototype hardware uses a commercially available field programmable analog array and Particle Swarm Optimization as the tuning method. We developed a scheme to correct the variance in measurement. We show that a self-tuned controller can outperform a handtuned solution and demonstrate adaptability to plant drift.