Constrained Iterative Learning Control with PSO-Youla Feedback Tuning for Building Temperature Control * * This work was supported by the National Foundation of Research (NRF) of Singapore and China Scholarship Council (CSC) Scholarship.

摘要

In building temperature control, one of the main challenges is to maintain good performance in the presence of disturbances from both outdoor weather and indoor human activities. Considering the fact that such disturbances contain both repetitive patterns and non-repetitive uncertainties, this paper proposes a two-degree-of-freedom (2-DOF) iterative control algorithm that synthesizes iterative learning control (ILC) and iterative feedback tuning (IFT) together. Based on lifting technique and Youla-parametrization, the 2-DOF controller design, i.e., the design of ILC and IFT, is formulated into one constrained optimization problem where an optimal combination of the learning matrix and feedback controller is found in each iteration with guaranteed stability. To make the synthesized problem solvable, techniques such as stabilizing projection and particle swarm optimization (PSO) are utilized in finding the optimal solutions. The effectiveness of the proposed approach is verified by simulations on a four-room building control testbed system.