Model system identification and adaptive controller design for R600a vapour compression refrigeration system

The research undertaken in this thesis is an investigation of the model system identification and adaptive controller design for R600a vapour compression refrigeration system. A refrigeration system removes heat from a thermally insulated compartment to keep the temperature inside the chamber below the ambient temperature and maintain it at a constant temperature with minimal variation or change in temperature. To address global ecological goals, a more environmentally friendly and energy-efficient refrigerant, R600a, has been proposed to replace R134a. R600a boasts a very low ozone depletion potential (ODP) and a global warming potential (GWP) of less than 20. However, the increased sensitivity of R600a to changes in temperature poses a critical challenge. Even slight pressure fluctuations can result in significant temperature variations. Therefore, meticulous management of the R600a refrigerant pressure is crucial for maintaining consistent and reliable system operation. To accommodate its usage, a Variable Speed Compressor and an Adaptive PID controller were developed in this research. To define the parameters of the adaptive controller, the modelling of the R600a characteristics was first performed through a closed-loop identification method using an autoregressive moving average with exogenous input (ARX) model and an autoregressive moving average with exogenous input (ARMAX) model. From the research conducted, it can be concluded that the best model for the vapour compression refrigeration system with a variable-speed compressor running R600a refrigerant setup in this research is ARMAX with a polynomial order of 221. ARMAX 221 has the best fit of 82.01%, a Final Prediction Error (FPE) of 0.02728, and a mean square error (MSE) of 0.02725. Based on the developed ARMAX model, it is then concluded that the best PID-based adaptive controller for this setup is an adaptive PD controller, which has a settling time of 972 s, a Steady State Error (SSE) of 0±0.30oC an overshoot of 1.8oC at the Peak time of 714 s. In conclusion, all the objectives of this research have been achieved. First, the vapour compression refrigeration system has been modified to have Variable Speed Compressor capability. Second, a real time black box model of VCRS using an R600a refrigerant system has been developed, which improves the accuracy of cooling control. Finally, an adaptive controller for the variable speed compressor of the vapour compression refrigeration system using R600a refrigerant has designed. The major contributions of this thesis are the model-tuned adaptive controller has a good transient response and a high degree of stability, as demonstrated by the real-time implementation results and the experiment demonstrated that the controller is capable of successfully tracking, regulating, and rejecting disturbances at a set point.