Due to the fact that cascade control can improve the single-loop’s performance well and reduce the integral error from disturbance response, it has been one of the most important control strategies in industrial production, especially in thermal power plant and chemical engineering. However, most of the existing research is based on the Gaussian system and other few studies on the non-Gaussian cascade disturbance system also have obvious defects. In this paper, an effective control loop performance assessment (CPA) of cascade control system for many non-Gaussian distributions even the unknown mixture disturbance noise has been proposed. Compared to the minimum variance control (MVC) approach, the minimum entropy control (MEC) method can obtain a more accurate estimate. In this method, like MVC, the primary loop output and secondary loop output can be represented as invariant and dependent terms, then adopted estimated distribution algorithm (EDA) is used to achieve the system model and disturbances. In order to show the effectiveness of MEC, some simulation examples based on different perturbations are given.
Application of the genetic algorithm for cascade control of a HVAC system