Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applica- tions. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to en- hance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorpo- rated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark exam- ple in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.This work was partially supported by the fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) and by EVO-CONTROL project (ref. PROMETEO/2012/028, Generalitat Valenciana - Spain).Reynoso Meza, G.; Sanchís Saez, J.; Blasco, X.; Freire, RZ. (2016). Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning. Expert Systems with Applications. 51:120-133. https://doi.org/10.1016/j.eswa.2015.11.0281201335