PID autotuner design using machine learning

A method using machine learning to automatically produce controller tuning algorithms is described. The method constitutes a decision tree which selects from a set of tuning rules the rule which is best able to improve controller characteristics. The decision tree is constructed using a training set of example systems. Evaluations of the resulting tuning algorithm are performed using a large independently generated set of example systems