Self-learning Backlash Inverse Control of Cooling or Heating Coil Valves Having Backlash Hysteresis

Valves are widely used in HVAC systems to regulate liquid flow rate, such as hot and chilled-water valves utilized in cooling and heating coils. These valves are typically controlled with motorized actuators where significant backlash hysteresis might exist and the backlash magnitude mostly depends on the clearance of the manufactured gearbox. Due to the hysteresis effect, unsatisfactory tracking performance results when using a conventional PI controller. To understand the control effect from the backlash hysteresis, this paper develops an emulator model for a cooling coil and valve set derived from field measurements. Based on observations of the simulation results, a selflearning backlash inverse control approach is proposed to mit igate the backlash effect with moderate modifications to a conventional PI control. In the proposed approach, a self-learn ing procedure is carried out at the beginning of the control implementation period to estimate the backlash magnitude for a specific valve. Then a backlash inverse block is added to an existing PI controller to compensate for the hysteresis effect residing in the valve. The validity of the proposed method was verified with both simulation and experimental tests and significant improvement was observed in the control performance.