Valve control, valve backlash hysteresis, nonlinear feedback control, self-learning control
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. This paper proposes a self-learning backlash inverse control approach to mitigate the backlash effect with moderate modifications to a conventional PI control. In the proposed approach, a self-learning 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.