C. Rieger and D.S. Naidu
Optimal tracking, fuzzy logic (FL), neural network (NN), linear quadratic tracking (LQT), HVAC
Many industrial facilities utilize pressure control gradients to prevent migration of hazardous species from containment areas to occupied zones, often using proportional-integral-derivative (PID) control. Within these facilities, PID control is often inadequate to maintain desired performance due to changing operating conditions, a common issue in process applications [1]. As the goal of the heating, ventilation and air-conditioning (HVAC) control system is to optimize the pressure gradients and associated flows for the plant, linear quadratic tracking (LQT) provides a time-based approach to guiding plant interactions. However, LQT methods are susceptible to modelling and measurement errors, and therefore a hybrid design using the integration of soft control methods with hard control methods is developed and demonstrated to account for these errors and non-linearities.
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