Robust Temperature Control of a Variable-Speed Refrigeration System Based on Sliding Mode Control with Optimal Parameters Derived Using the Genetic Algorithm

A sliding mode control (SMC) technique based on a state observer with a Kalman filter and feedforward controller was established for a variable-speed refrigeration system (VSRS) to ensure robust control against model uncertainties and disturbances, including noise. The SMC was designed using a state-space model transformed from a practical transfer function model, which was derived by conducting dynamic characteristic experiments. Fewer parameters affecting the model uncertainty were required to be identified, which facilitated modeling. The state observer for estimating the state variables was designed using a Kalman filter to ensure robustness against noise. A feedforward controller was added to the control system to compensate for the deterioration in the transient characteristics due to the saturation function used to avoid chattering. A genetic algorithm was used to alleviate the trial and error involved in determining the design parameters of the saturation function and select optimal values. Simulations and experiments were conducted to verify the control performance of the proposed SMC. The results show that the proposed controller can realize robust temperature control for a VSRS despite stepwise changes in the reference and external heat load, and avoid the trial and error process to design parameters for the saturation function.