Unfalsified PI control design for a weigh belt feeder using genetic search

This paper proposes and experimentally demonstrates an approach to automated PI tuning for an industrial weigh belt feeder that is based on unfalsified control concepts. Unfalsified control is used here as a means of using either open or closed loop test data to identify a subset of controllers (from an initial set) that is not proved to violate the multiple objectives specified by the control engineer. A novel feature of the unfalsified approach is that it allows controllers to be eliminated from consideration by predicting their performance without actually inserting the controllers in the loop. In addition, this methodology does not require an explicit model. However, in practice it does require some closed-loop experimentation to determine the cost functions used to perform the unfalsification. When the unfalsified PI autotuning approach is applied to the industrial weight belt feeder, it is able to successfully identify a subset of PI control laws that meets the performance specs. A key feature of this paper is the use of a genetic search algorithm to reduce the computational requirements of unfalsified control, especially when the initial set of controllers is large