C. Rieger and D.S. Naidu (USA)
Optimal, Tracking, Fuzzy Logic (FL), Neural Network (NN), 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 systems. When operators rebalance the plant, variation from the desired gradients can occur and the operating conditions can change enough that the PID parameters are no longer adequate to maintain a stable system. As the goal of the ventilation control system is to optimize the pressure gradients and associated flows for the plant, Linear Quadratic Tracking (LQT) is a method that provides a time-based approach to guiding plant interactions. However, LQT methods are susceptible to modeling and measurement errors, and therefore the additional use of soft computing methods is proposed for implementation to account for these errors and nonlinearities. The performance of the resulting hybrid controller is demonstrated through simulation and experimental testing as compared to a representative PID controller.
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