Abstract
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.
Highlights
Variable-speed refrigeration systems (VSRSs) are widely used in various industrial fields due to their excellent energy-saving ability and high-precision temperature control capability [1,2,3,4,5,6,7]
VSRSs, which are composed of a variable speed compressor, an electronic expansion valve (EEV) and heat exchangers, have inherent nonlinear characteristics
As a dynamic model of VSRSs used to realize model-based control, an analytical model derived by applying the governing equation of Navier–Stokes to a heat exchanger was proposed [12]. Because this model is a high-order nonlinear partial differential equation, model uncertainty is generated in the process of linearization and low-dimensional modeling, and the model uncertainty increases in the process of parameter identification of the model
Summary
Variable-speed refrigeration systems (VSRSs) are widely used in various industrial fields due to their excellent energy-saving ability and high-precision temperature control capability [1,2,3,4,5,6,7]. As a dynamic model of VSRSs used to realize model-based control, an analytical model derived by applying the governing equation of Navier–Stokes to a heat exchanger was proposed [12] Because this model is a high-order nonlinear partial differential equation, model uncertainty is generated in the process of linearization and low-dimensional modeling, and the model uncertainty increases in the process of parameter identification of the model. X is a virtual physical quantity generated by converting the model of the plant into the controllable canonical form presented in Equations (4) and (5) These values are estimated instead of being detected by designing a state observer for control. FFiigguurree44..MMAATTLLAABB//SSiimmuulliinnkk pprrooggrraamm ffoorr ssiimmuullaattiioonnss aanndd eexxppeerriimmeennttss ttoo ccoonnttrrooll To.. ((aa)) SSiimmuullaattiioonn.. ((bb))EExxppeerriimmeenntt
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