Parameter tuning of air conditioning system models is a critical step in constructing accurate dynamic models for use in control design and optimization. However, traditional manual or simulation-based methods of parameter tuning are tedious, time-consuming, or simply infeasible due to the large number of parameters. In this paper, an approach to tune multiple parameters of HVAC&R systems is proposed and shown to be effective in practice. An accurate and computationally efficient model is derived, and a wavelet decomposition approach is adopted in formulating the cost function that seeks to minimize the error between the predicted and measured data. Wavelets are advantageous in handling the multi-domain signals and capturing large and small dynamic features. In order to reduce the optimization time, a hybrid parameter tuning method is proposed. This method first uses a stochastic global tuning method (genetic algorithm) to find a set of estimated parameters, and these parameters serve as the initial values of a gradient search method. The results show that the proposed method can effectively tune many simultaneous parameters using large data sets.
- Dynamic Systems and Control Division
Multi-Parametric Tuning of Dynamic Air Conditioning Models Using Experimental Data
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Liang, S, Rasmussen, BP, & Kaplan, MF. "Multi-Parametric Tuning of Dynamic Air Conditioning Models Using Experimental Data." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 519-528. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8755
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