The variable air volume (VAV) air conditioning system is with strong coupling and large time delay, for which model predictive control (MPC) is normally used to pursue perfor-mance improvement. Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response, a novel tuning method based on machine learning and improved particle swarm optimization (PSO) is proposed. In this method, the relationship between MPC controller parameters and time domain performance indices is estab-lished via machine learning. Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices. In addition, the PSO algorithm is further modi-fied under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method. Finally, the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.

Ning He;Kun Xi;Mengrui Zhang;Shang Li

School of Mechanical and Electrical Engineering,Xi'an Uni-versity of Architecture and Technology,Xi'an 710055,China

北京理工大学学报（英文版）

[1]Ning He;Kun Xi;Mengrui Zhang;Shang Li-.A Novel Tuning Method for Predictive Control of VAV Air Conditioning System Based on Machine Learning and Improved PSO)[J].北京理工大学学报（英文版）,2022(04):350-361

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