Human Operator Based Fuzzy Intuitive Controllers Tuned with Genetic Algorithms

A recent study (Desborough and Miller, 2001) revealed that a great majority of the control loops that operate in industry use the PID (Proportional-Integral-Derivative) controllers. Furthermore, the study has shown that more than one third of these loops were switched to manual for a considerable period of time, indicating poor behaviour of the controllers' performance. As was also reported, the gap between the industrial practice and the process control theory remains unchanged over the years, indicating that industry is looking for simple and easy to use technologies. The present research offers an alternative control scheme that intends to be a step towards introducing a new technology for practical implementation in industry. The controller is developed aiming to emulate human operators' actions when manually controlling SISO systems, subject to disturbances. The developed control scheme is based on an intuitive hypothetical model that describes the way human operators (HO) act in a manual control loop, generating the Human Operator Based Intuitive Controller (HOBIC). Since human operators typically use vague terms when describing control actions, it is natural to use fuzzy logic to express manual control actions. The HOBIC is then extended using the Fuzzy Logic theory. Membership functions within Fuzzy-HOBIC are tuned using a genetic algorithm (GA). The tuning does not require a process model. It is based on historical process operation data containing manual operation actions from experienced operators. The traditional GA is modified to cope with real valued optimisation variables and their constraints. Results show that the hypothetic model created for the HO's actions is appropriate, since the generated control actions by the HOBIC and Fuzzy-HOBIC can approximate those of human operators. The control signal generated has the same discontinuous nature of the HO's one.