Dynamic control of a hybrid control valve

Abstract

In industries around the world control valves are commonly used to control integrated systems. The operational range of a control valve may therefore extend over large excursions of pressure and fluid or gas density. These valves should be able to exert strict control over pressurised gasses or heated fluids in order to keep systems safe and manageable. However, the desired operational range and accuracy guaranteeing peak performance is often not available in a single valve, and therefore a combination of valves may be considered to achieve this. The purpose of this project is to develop an optimised algorithm that will control such a hybrid control valve system. The algorithm should optimise the coordination of two separate valves, of which the maximum flow coefficients differ by a large degree. This should be done in such a way that the two valves essentially function as a single valve with new characteristics. The new valve should subsequently be able to accurately function over both small and large ranges of mass flow. Two different hybrid valve controllers are discussed. The first is a linear, PID based controller that is designed for simple inputs. The controller's main task is to assist in developing a better comprehension of the main challenges that will be faced in coordinating the operation of two separate valves. Although the controller provides stable control for step inputs with a low frequency of change, it is seen that it fails to deliver satisfactory results when faced with complex request signals. It is consequently concluded that more complex control will be required. The second hybrid valve controller is therefore designed with the purpose of controlling more sophisticated input request signals. The controller's design is based on Fuzzy Logic which provides an effective platform for complex control. The complete control system consists of four main elements: The low pass filter, which filters out unachievable high frequencies from the input request, the Neural Network based signal predictors, which increases the efficiency of the controller, the Fuzzy Inference System, which is responsible for all the control decisions, and the crisp controller, which aids the Fuzzy Inference System with control executions that cannot be fuzzified. The final optimisation to the Fuzzy Logic based hybrid valve controller is done by Genetic Algorithms. The membership functions that lend itself to optimisation are identified, and their parameters are optimised in order to further minimise the controller's mean control error.