Adaptive-model predictive control of electronic expansion valves with adjustable setpoint for evaporator superheat minimization

In many refrigeration and air-conditioning systems, the automatic controller in electronic expansion valves have been employed as a component responsible for controlling the valve opening so that the superheat at the outlet of the evaporator remains within the desired limits. In some of these controllers, the control parameters are tuned once for a certain operating point and remain unaltered, even when the operating conditions change, unless the operator changes it manually. For a strongly nonlinear plant with a dramatically time varying characteristics, linear time invariant (LTI) prediction accuracy might degrade significantly that the performance of traditional Model Predictive Controller (MPC) becomes unacceptable. This work presents an Adaptive-Model Predictive Control (AMPC) mechanism to address this degradation where the parameters are tuned continuously through recursive estimation and update approaches, making the MPC insensitive to prediction errors and to achieve the optimal superheat response. Moreover, an adaptive setpoint hunting algorithm is implemented so that the system achieves stability and improves energy efficiency simultaneously. •An online adaptive-model mechanism is demonstrated to model HVAC system.•An MPC controller utilizes the adaptive model for controlling EEV valve.•A hunting algorithm for optimal superheat set point selection is designed.•High controller stability and fast response time were experimentally demonstrated.