Self-tuning of PID controllers by adaptive interaction

We propose a new self-tuning or adaptation algorithm for PID controllers based on a theory of adaptive interaction. The theory develops a simple and effective way to perform gradient descent in the parameter space. One version of the tuning algorithm requires no knowledge of the plant to be controlled. This makes the algorithm robust to changes in the plant. It also makes the algorithm universally applicable to linear and nonlinear plants. The algorithm achieves the tuning objective by minimizing an error function. Because of its simplicity, the overhead for adding self-tuning is negligible. We applied this algorithm in an automotive product manufactured by Hitachi to satisfy performance requirements for both cold start and normal operation. Simulation results are presented to show the validity of the approach